Video entity recognition in compressed digital video streams

ABSTRACT

A method and system for detection of video segments in compressed digital video streams is presented. The compressed digital video stream is examine to determine synchronization points, and the compressed video signal is analyzed following detection of the synchronization points to create video fingerprints that are subsequently compared against a library of stored fingerprints.

CROSS REFERENCE TO RELATED APPLICATION

This application is a continuation-in-part of co-pending U.S. patentapplication Ser. No. 11/067,606, filed Feb. 25, 2005 and entitled“Detecting Known Video Entities Utilizing Fingerprints”, which is acontinuation-in-part of U.S. patent application Ser. No. 10/790,468,filed Mar. 1, 2004 and entitled “Video Detection and Insertion”, whichclaims the benefit of U.S. Provisional Application 60/452,802, filedMar. 7, 2003, entitled “System and Method for Advertisement Substitutionin Broadcast and Prerecorded Video Streams” and U.S. ProvisionalApplication 60/510,896, filed Oct. 14, 2003, entitled “Video Detectionand Insertion”, the entire disclosures of which are herein incorporatedby reference.

This application claims the benefit of U.S. Provisional PatentApplication No. 60/671,380 filed Apr. 14, 2005, and entitled “VideoEntity Recognition in Compressed Digital Video Streams”, the entiredisclosure of which is herein incorporated by reference.

COPYRIGHT NOTICE AND AUTHORIZATION

Portions of the documentation in this patent document contain materialthat is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure as it appears in the Patent and TrademarkOffice file or records, but otherwise reserves all copyright rightswhatsoever.

BACKGROUND OF THE INVENTION

Detection of video segments is used to recognize known video segmentsand take subsequent video processing steps. In one example, specificadvertisements or video scenes are detected in a video stream andsubstituted or deleted from the video stream. In other applications itis desirable to recognize certain scenes for other purposes such asvideo indexing or the creation of other metadata or other referencematerial attached to that particular video scene. In all of theseexamples it is necessary to be able to recognize a known video segment.

Methods have been developed which can be used to detect known videosequences. These methods include recognition of certain characteristicsassociated with scene changes or certain video segments as well as thecomparison of video segments with fingerprints of those video segments.In the fingerprinting technique, the known video segments arecharacterized and the incoming video stream is compared with thecharacterizations to determine if a known video sequence is in factpresent at that time.

One technique for recognizing video segments is to create a colorcoherence vector (CCV) or a low-res image (e.g., of size 8 by 8 pixels)representation of a known video sequence and compare the CCV or low-resimage fingerprint against the color coherence vectors or low-res imagesof incoming video streams. Other techniques can be used to compare theincoming video to stored fingerprints but all of the known presentlyused techniques are based on operations performed in the uncompresseddomain. This requires that the video be completely decompressed in orderto calculate the specific parameters of the fingerprint and perform acomparison. Even in the instances in which there is a partialdecompression, specific algorithms performed on the decompressed streamneed to be performed to compare the incoming video stream with thefingerprint. It is desirable to have a method and system of detectingvideo sequences in compressed digital video streams prior to theirdecompression.

BRIEF SUMMARY OF THE INVENTION

The present method and system is based on the use of statisticalparameters of compressed digital video streams for the recognition ofknown video segments by comparison against fingerprints. The method andsystem can be used to identify the video segments by comparingstatistical parameters of the compressed stream with fingerprints ofknown video sequences, those fingerprints containing parameters relatedto the compressed stream. The technique may also be used in conjunctionwith fingerprinting techniques based on the uncompressed domain suchthat a partial comparison is made in the compressed domain and asubsequent comparison is made in the uncompressed domain. One of theadvantages of the present method and system is that it allows for moreefficient and rapid identification of known video sequences and does notrely on processing completely in the uncompressed domain.Synchronization can be obtained from the compressed digital videosignal. In other words, the compressed fingerprint may serve as a fastpre-filter to find matching candidates, while the (slower) comparison inthe uncompressed domain is used for verification. Based on the detectionof synchronization points, subsequent signal processing is facilitatedto provide efficient fingerprinting of the incoming digital videosignal.

The present method and system is used to detect a known video entitywithin a compressed digital video stream. The compressed digital streamis received, and synchronization points are determined within thecompressed stream. Statistical parameterized representations of thecompressed digital video stream for windows following thesynchronization points in the video stream are created, and compared towindows of a plurality of fingerprints that includes associatedstatistical parameterized representations of known video entities. Aknown video entity is detected in the compressed digital video streamwhen at least one of the plurality of fingerprints has at least athreshold level of similarity to fingerprint created from the videostream.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The foregoing summary, as well as the following detailed description ofpreferred embodiments of the invention, will be better understood whenread in conjunction with the appended drawings. For the purpose ofillustrating the invention, there is shown in the drawings embodimentswhich are presently preferred. It should be understood, however, thatthe invention is not limited to the precise arrangements andinstrumentalities shown.

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawing(s) will be provided by the Office upon request and paymentof the necessary fee.

In the Drawings:

FIG. 1 is a diagram of a content delivery system, according to oneembodiment of the present invention;

FIG. 2 is a diagram of a configuration for local detection ofadvertisements within a video programming stream used in the contentdelivery system of FIG. 1, according to one embodiment of the presentinvention;

FIG. 3 shows an exemplary pixel grid for a video frame and an associatedcolor histogram, according to one embodiment of the present invention;

FIG. 4 shows an exemplary comparison of two color histograms, accordingto one embodiment of the present invention;

FIG. 5 shows an exemplary pixel grid for a video frame and associatedcolor histogram and color coherence vector, according to one embodimentof the present invention, according to one embodiment of the presentinvention;

FIG. 6A shows an exemplary comparison of color histograms and CCVs fortwo images, according to one embodiment of the present invention;

FIG. 6B shows comparison of edge pixels for two exemplary consecutiveimages, according to one embodiment of the present invention;

FIG. 6C shows comparison of the movement of macroblocks for twoexemplary consecutive images, according to one embodiment of the presentinvention;

FIG. 7 shows an exemplary pixel grid for a video frame with a pluralityof regions sampled and the determination of the average color for aregions, according to one embodiment of the present invention;

FIG. 8 shows two exemplary pixel grids having a plurality of regions forsampling and coherent and incoherent pixels identified, according to oneembodiment of the present invention;

FIG. 9 shows exemplary comparisons of the pixel grids of FIG. 8 based oncolor histograms for the entire frame, CCVs for the entire frame andaverage color for the plurality of regions, according to one embodimentof the present invention;

FIG. 10 is a block diagram of an advertisement matching process,according to one embodiment of the present invention;

FIG. 11 is a block diagram of an initial dissimilarity determinationprocess, according to one embodiment of the present invention;

FIG. 12 shows an exemplary initial comparison of calculated features foran incoming stream versus initial portions of fingerprints for aplurality of known advertisements, according to one embodiment of thepresent invention;

FIG. 13 shows an exemplary initial comparison of calculated features foran incoming stream, similar to FIG. 12, with an expanded initial portionof a fingerprint for a known advertisement, according to one embodimentof the present invention;

FIG. 14 shows an exemplary expanding window comparison of the featuresof the incoming video stream and the features of the fingerprints ofknown advertisements, according to one embodiment of the presentinvention;

FIG. 15 shows an exemplary pixel grid divided into sections, accordingto one embodiment of the present invention;

FIG. 16 shows an exemplary comparison of two whole images andcorresponding sections of the two images, according to one embodiment ofthe present invention;

FIG. 17 shows an exemplary comparison of pixel grids by sections,according to one embodiment of the present invention;

FIG. 18A shows two images with different overlays, according to oneembodiment of the present invention;

FIG. 18B shows two additional images with different overlays, accordingto one embodiment of the present invention;

FIG. 19A shows an exemplary impact on pixel grids of an overlay beingplaced on corresponding image, according to one embodiment of thepresent invention;

FIG. 19B shows an exemplary pixel grid with a region of interestexcluded, according to one embodiment of the present invention;

FIG. 20 shows an exemplary image to be fingerprinted that is dividedinto four sections and has a portion to be excluded from fingerprinting,according to one embodiment of the present invention;

FIG. 21 shows an exemplary image to be fingerprinted that is dividedinto a plurality of regions that are evenly distributed across the imageand has a portion to be excluded from fingerprinting, according to oneembodiment of the present invention;

FIG. 22A shows an exemplary channel change image where a channel banneris a region of disinterest, according to one embodiment of the presentinvention;

FIG. 22B shows an exemplary channel change image where channelidentification information contained in a channel banner is a region ofinterest, according to one embodiment of the present invention;

FIG. 23 shows an image with expected locations of a channel banner andchannel identification information within the channel banner identified,according to one embodiment of the present invention;

FIG. 24 shows the family of feature based detection methods as well asrecognition, and specifically fingerprint, detection methods, accordingto one embodiment of the present invention;

FIG. 25 is a diagram of a spatial compression process for digital videowith associated statistically relevant parameters, according to anembodiment of the present invention;

FIG. 26 is a table of size and run length parameters being converted tocode words for transmission as indicated the process in FIG. 25,according to one embodiment of the present invention;

FIG. 27 is a block diagram showing a method for video entityrecognition, according to an embodiment of the present invention;

FIG. 28 shows a spatially coded transmission stream created with theencoding demonstrated in FIG. 26, with associated statistically relevantparameters, according to an embodiment of the present invention;

FIG. 29 shows a temporally encoded transmission stream with associatedstatistically relevant parameters, according to an embodiment of thepresent invention; and

FIG. 30 shows synchronization points within a compressed transmissionstream used to trigger creation of statistical parameter as described inFIGS. 25 and 29, according to an embodiment of the present invention.

DETAILED DESCRIPTION OF THE INVENTION

Certain terminology is used herein for convenience only and is not to betaken as a limitation on the present invention. In the drawings, thesame reference letters are employed for designating the same elementsthroughout the several figures.

An exemplary content delivery system 100 is shown in FIG. 1. The system100 includes a broadcast facility 110 and receiving/presentationlocations. The broadcast facility 110 transmits content to thereceiving/presentation facilities and the receiving/presentationfacilities receive the content and present the content to subscribers.The broadcast facility 110 may be a satellite transmission facility, ahead-end, a central office or other distribution center. The broadcastfacility 110 may transmit the content to the receiving/presentationlocations via satellite 170 or via a network 180. The network 180 may bethe Internet, a cable television network (e.g., hybrid fiber cable,coaxial), a switched digital video network (e.g., digital subscriberline, or fiber optic network), broadcast television network, other wiredor wireless network, public network, private network, or somecombination thereof. The receiving/presentation facilities may includeresidence 120, pubs, bars and/or restaurants 130, hotels and/or motels140, business 150, and/or other establishments 160.

In addition, the content delivery system 100 may also include a DigitalVideo Recorder (DVR) that allows the user (residential or commercialestablishment) to record and playback the programming. The methods andsystem described herein can be applied to DVRs both with respect tocontent being recorded as well as content being played back.

The content delivery network 100 may deliver many different types ofcontent. However, for ease of understanding the remainder of thisdisclosure will concentrate on programming and specifically videoprogramming. Many programming channels include advertisements with theprogramming. The advertisements may be provided before and/or after theprogramming, may be provided in breaks during the programming, or may beprovided within the programming (e.g., product placements, bugs, bannerads). For ease of understanding the remainder of the disclosure willfocus on advertisements opportunities that are provided betweenprogramming, whether it be between programs (e.g., after one program andbefore another) or during programming (e.g., advertisement breaks inprogramming, during time outs in sporting events). The advertisementsmay subsidize the cost of the programming and may provide additionalsources of revenue for the broadcaster (e.g., satellite serviceprovider, cable service provider).

In addition to being able to recognize advertisements it is alsopossible to detect particular scenes of interest or to genericallydetect scene changes. A segment of video or a particular image, or scenechange between images, which is of interest, can be considered to be avideo entity. The library of video segments, images, scene changesbetween images, or fingerprints of those images can be considered to becomprised of known video entities.

A variety of mechanisms for detection of video entities and subsequentlymechanisms for defeating the automated detection of video entities suchas intros, outros, and advertisements are discussed herein.

As the advertisements provided in the programming may not be appropriateto the audience watching the programming at the particular location,substituting advertisements may be beneficial and/or desired.Substitution of advertisements can be performed locally (e.g., residence120, pub 130, hotel 140) or may be performed somewhere in the videodistribution system 100 (e.g., head end, nodes) and then delivered to aspecific location (e.g., pub 130), a specific geographic region (e.g.,neighborhood), subscribers having specific traits (e.g., demographics)or some combination thereof. For ease of understanding, the remainingdisclosure will focus on local substitution as the substitution anddelivery of targeted advertisements from within the system 100.

Substituting advertisements requires that advertisements be detectedwithin the programming. The advertisements may be detected usinginformation that is embedded in the program stream to define where theadvertisements are. For analog programming cue tones may be embedded inthe programming to mark the advertisement boundaries. For digitalprogramming digital cue messages may be embedded in the programming toidentify the advertisement boundaries. Once the cue tones or cue tonemessages are detected, a targeted advertisement or targetedadvertisements may be substituted in place of a default advertisement,default advertisements, or an entire advertisement block. The localdetection of cue tones (or cue tone messages) and substitution oftargeted advertisements may be performed by local system equipmentincluding a set top box (STB) or DVR. However, not all programmingstreams include cue tones or cue tone messages. Moreover, cue tones maynot be transmitted to the STB or DVR since the broadcaster may desire tosuppress them to prevent automated ad detection (and potentialdeletion).

Techniques for detecting advertisements without the use of cue tones orcue messages include manual detection (e.g., individuals detecting thestart of advertisements) and automatic detection. Regardless of whattechnique is used, the detection can be performed at various locations(e.g., pubs 130, hotels 140). Alternatively, the detection can beperformed external to the locations where the external detection pointsmay be part of the system (e.g., node, head end) or may be external tothe system. The external detection points would inform the locations(e.g., pubs 130, hotels 140) of the detection of an advertisement oradvertisement block. The communications from the external detectionpoint to the locations could be via the network 170. For ease ofunderstanding this disclosure, we will focus on local detection.

An exemplary configuration for manual local detection of advertisementswithin a video programming stream is shown in FIG. 2. The incoming videostream is received by a network interface device (NID) 200. The type ofnetwork interface device will be dependent on how the incoming videostream is being delivered to the location. For example, if the contentis being delivered via satellite (e.g., 170 of FIG. 1) the NID 200 willbe a satellite dish (illustrated as such) for receiving the incomingvideo stream. The incoming video stream is provided to a STB 210 (atuner) that tunes to a desired channel, and possibly decodes the channelif encrypted or compressed. It should be noted that the STB 210 may alsobe capable of recording programming as is the case with a DVR or videocassette recorder VCR.

The STB 210 forwards the desired channel (video stream) to a splitter220 that provides the video stream to a detection/replacement device 230and a selector (e.g., A/B switch) 240. The detection/replacement device230 detects and replaces advertisements by creating a presentationstream consisting of programming with targeted advertisements. Theselector 240 can select which signal (video steam or presentationstream) to output to an output device 250 (e.g., television). Theselector 240 may be controlled manually by an operator, may becontrolled by a signal/message (e.g., ad break beginning message, adbreak ending message) that was generated and transmitted from anupstream detection location, and/or may be controlled by thedetection/replacement device 230. The splitter 220 and the selector 240may be used as a bypass circuit in case of an operations issue orproblem in the detection/replacement device 230. The default mode forthe selector 240 may be to pass-through the incoming video stream.

Manually switching the selector 240 to the detection/replacement device230 may cause the detection/replacement device 230 to provideadvertisements (e.g., targeted advertisements) to be displayed to thesubscriber (viewer, user). That is, the detection/replacement device 230may not detect and insert the advertisements in the program stream tocreate a presentation stream. Accordingly, the manual switching of theselector 240 may be the equivalent to switching a channel from a programcontent channel to an advertisement channel. Accordingly, this systemwould have no copyright issues associated therewith as no recording,analyzing, or manipulation of the program stream would be required.

While the splitter 220, the detection/replacement device 230, and theselector 240 are all illustrated as separate components they are notlimited thereby. Rather, all the components could be part of a singlecomponent (e.g., the splitter 220 and the selector 240 contained insidethe detection/replacement device 230; the splitter 220, thedetection/replacement device 230, and the selector 240 could be part ofthe STB 210).

Automatic techniques for detecting advertisements (or advertisementblocks) may include detecting aspects (features) of the video streamthat indicate an advertisement is about to be displayed or is beingdisplayed (feature based detection). For example, advertisements areoften played at a higher volume than programming so a sudden volumeincrease (without commands from a user) may indicate an advertisement.Many times several dark monochrome (black) frames of video are presentedprior to the start of an advertisement so the detection of these typesof frames may indicate an advertisement. The above noted techniques maybe used individually or in combination with one another. Thesetechniques may be utilized along with temporal measurements, sincecommercial breaks often begin within a certain known time range.However, these techniques may miss advertisements if the volume does notincrease or if the display of black frames is missing or does not meet adetection threshold. Moreover, these techniques may result in falsepositives (detection of an advertisement when one is not present) as theprogramming may include volume increases or sequences of black frames.

Frequent scene/shot breaks are more common during an advertisement sinceaction/scene changes stimulate interest in the advertisement.Additionally, there is typically more action and scene changes during anadvertisement block. Accordingly, another possible automatic featurebased technique for detecting advertisements is the detection ofscene/shot breaks (or frequent scene/shot breaks) in the videoprogramming. Scene breaks may be detected by comparing consecutiveframes of video. Comparing the actual images of consecutive frames mayrequire significant processing. Alternatively, scene/shot breaks may bedetected by computing characteristics for consecutive frames of videoand for comparing these characteristics. The computed characteristicsmay include, for example, a color histogram or a color coherence vector(CCV). The detection of scene/shot breaks may result in many falsepositives (detection of scene changes in programming as opposed toactual advertisements).

A color histogram is an analysis of the number of pixels of variouscolors within an image or frame. Prior to calculating a color histogramthe frame may be scaled to a particular size (e.g., a fixed number ofpixels), the colors may be reduced to the most significant bits for eachcolor of the red, blue, green (RGB) spectrum, and the image may besmoothed by filtering. As an example, if the RGB spectrum is reduced tothe 2 most significant bits for each color (4 versions of each color)there will be a total of 6 bits for the RGB color spectrum or 64 totalcolor combinations (2⁶).

An exemplary pixel grid 300 for a video frame and an associated colorhistogram 310 is shown in FIG. 3. As illustrated the pixel grid 300 is4×4 (16 pixels) and each grid is identified by a six digit number witheach two digit portion 320 representing a specific color (RGB). Belowthe digit is the color identifier for each color 330. For example, anupper left grid has a 100000 as the six digit number which equates toR₂, G₀ and B₀. As discussed, the color histogram 310 is the number ofeach color in the overall pixel grid. For example in FIG. 3, the 9 R₀'sin the pixel grid 300 are indicated in the first column 340 of the colorhistogram 310.

FIG. 4 shows an exemplary comparison of two color histograms 400, 410.The comparison entails computing the difference/distance between thetwo. The distance may be computed for example by summing the absolutedifferences (L1-Norm) 420 or by summing the square of the differences(L2-Norm) 430. For simplicity and ease of understanding we assume thatthe image contains only 9 pixels and that each pixel has the same bitidentifier for each of the colors in the RGB spectrum such that eachcolor is represented by a single number. The difference between thecolor histograms 400, 410 is 6 using the absolute difference method 420and 10 using the squared difference method 430. Depending on the methodutilized to compare the color histograms the threshold used to detectscene changes or other parameters may be adjusted accordingly.

A color histogram tracks the total number of colors in a frame. Thus, itis possible that when comparing two frames that are completely differentbut utilize similar colors throughout, a false match will occur. CCVsdivide the colors from the color histogram into coherent and incoherentones based on how the colors are grouped together. Coherent colors arecolors that are grouped together in more than a threshold number ofconnected pixels and incoherent colors are colors that are either notgrouped together or are grouped together in less than a threshold numberof pixels. For example, if 8 is the threshold and there are only 7 redpixels grouped (connected together) then these 7 red pixels areconsidered incoherent.

An exemplary pixel grid 500 for a video frame and associated colorhistogram 510 and CCVs 520, 530 is shown in FIG. 5. For ease ofunderstanding we assume that all of the colors in the pixel grid havethe same number associated with each of the colors (RGB) so that asingle number represents each color and the pixel grid 500 is limited to16 pixels. Within the grid 500 there are some colors that are groupedtogether (has at least one other color at a connected pixel—one of the 8touching pixels) and some colors that are by themselves. For example,two color 1s 540, four color 2s 550, and four (two sets of 2) color 3s560, 570 are grouped (connected), while three color 0s, one color 1, andtwo color 3s are not grouped (connected). The color histogram 510indicates the number of each color. A first CCV 520 illustrates thenumber of coherent and incoherent colors assuming that the thresholdgrouping for being considered coherent is 2 (that is a grouping of twopixels of the same color means the pixels are coherent for that color).A second CCV 530 illustrates the number of coherent and incoherentcolors assuming that the threshold grouping was 3. The colors impactedby the change in threshold are color 0 (went from 2 coherent and 1incoherent to 0 coherent and 3 incoherent) and color 3 (went from 4coherent and 2 incoherent to 0 coherent and 6 incoherent). Depending onthe method utilized to compare the CCVs the threshold used for detectingscene changes or other parameters may be adjusted accordingly.

FIG. 6A shows an exemplary comparison of color histograms 600, 602 andCCVs 604, 606 for two images. In order to compare, the differences(distances) between the color histograms and the CCVs can be calculated.The differences may be calculated, for example, by summing the absolutedifferences (L1-Norm) or by summing the square of the differences(L2-Norm). For simplicity and ease of understanding assume that theimage contains only 9 pixels and that each pixel has the same bitidentifier for each of the colors in the RGB spectrum. As illustratedthe color histograms 600, 602 are identical so the difference (ΔCH) 608is 0 (calculation illustrated for summing the absolute differences). Thedifference (ΔCCV) 610 between the two CCVs 604 606 is 8 (based on thesum of the absolute differences method).

Another possible feature based automatic advertisement detectiontechnique includes detecting action (e.g., fast moving objects, hardcuts, zooms, changing colors) as an advertisement may have more actionin a short time than the programming. According to one embodiment,action can be determined using edge change ratios (ECR). ECR detectsstructural changes in a scene, such as entering, exiting and movingobjects. The changes are detected by comparing the edge pixels ofconsecutive images (frames), n and n−1. Edge pixels are the pixels thatform a distinct boundary between two distinct objects or surfaces withina scene (e.g., a person, a house). A determination is made as to thetotal number of edge pixels for two consecutive images, σ_(n) andσ_(n−1), the number of edge pixels exiting a first frame, X_(n−1) ^(out)and the number of edge pixels entering a second image, X_(n) ^(in). TheECR is the maximum of (1) the ratio of outgoing edge pixels to totalpixels for a first image$\left( \frac{X_{n - 1}^{out}}{\sigma_{n - 1}} \right),$or (2) the ratio of incoming edge pixels to total pixels for a secondimage $\left( \frac{X_{n}^{in}}{\sigma_{n}} \right).$

Two exemplary consecutive images, n 620 and n−1 630 are shown in FIG.6B. Edge pixels for each of the images are shaded. The total number ofedge pixels for image n−1, σ_(n−1), is 43 while the total number of edgepixels for image n, σ_(n), is 33. The pixels circled 632, 634, and 636in image n−1 are not part of the image n (they exited image n−1).Accordingly, the number of edge pixels exiting image n−1, X_(n−1)^(out), is 22. The pixels circled 622, 624, and 626 in image n were notpart of image n−1 (they entered image n). Accordingly, the number ofedge pixels entering image n, X_(n) ^(in), is 12. The ECR 640 is thegreater of the two ratios$\frac{X_{n - 1}^{out}}{\sigma_{n - 1}}\left( \frac{22}{43} \right)$and $\frac{X_{n}^{in}}{\sigma_{n}}{\left( \frac{12}{33} \right).}$Accordingly, the ECR value is 0.512.

Action can be determined using a motion vector length (MVL). The MVLdivides images (frames) into macroblocks (e.g., 16×16 pixels). Adetermination is then made as to where each macroblock is in the nextimage (e.g., distance between macroblock in consecutive images). Thedetermination may be limited to a certain number of pixels (e.g., 20) ineach direction. If the location of the macroblock can not be determinedthen a predefined maximum distance may be defined (e.g., 20 pixels ineach direction). The macroblock length vector for each macroblock can becalculated as the square root of the sum of the squares of thedifferences between the x and y coordinates(√{square root over((x₁−x_(x))²+(y₁+y₂)²)}).

FIG. 6C shows two exemplary consecutive images, n 650 and n−1 660. Theimages are divided into a plurality of macroblocks 670 (as illustratedeach macroblock is 4 (2×2) pixels). Four specific macroblocks 672, 674,676, and 678 are identified with shading and are labeled 1-4 in theimage n−1 660. A maximum search area 680 is defined around the 4specific macroblocks as a dotted line (as illustrated the search areasis one macroblock in each direction). The four macroblocks 672, 674,676, and 678 are identified with shading on the image n 650. Comparingthe specified macroblocks between images 650 and 660 reveals that thefirst 672 and second macroblocks 674 moved within the defined searcharea, the third macroblock 676 did not move, and the fourth macroblock678 moved out of the search area. If the upper left hand pixel is usedas the coordinates for the macroblock it can be seen that MB1 moved from1,1 to 2,2; MB2 moved from 9,7 to 11,9; MB3 did not move from 5,15; andMB4 moved from 13,13 to outside of the range. Since MB4 could not befound within the search window a maximum distance of 3 pixels in eachdirection is defined. Accordingly, the length vectors 590 for themacroblocks are 1.41 for MB1, 2.83 for MB2, 0 for MB3, and 4.24 for MB4.

As with the other feature based automatic advertisement detectiontechniques the action detection techniques (e.g., ECR, MVL) do notalways provide a high level of confidence that the advertisement isdetected and may also led to false positives.

Several of these techniques may be used in conjunction with one anotherto produce a result with a higher degree of confidence and may be ableto reduce the number of false positives and detect the advertisementsfaster. However, as the feature based techniques are based solely onrecognition of features that may be present more often in advertisementsthan programming there can probably never be a complete level ofconfidence that an advertisement has been detected. In addition, it maytake a long time to recognize that these features are present (severaladvertisements).

In some countries, commercial break intros are utilized to indicate tothe viewers that the subsequent material being presented is notprogramming but rather sponsored advertising. These commercial breakintros vary in nature but may include certain logos, characters, orother specific video and audio messages to indicate that the subsequentmaterial is not programming but rather advertising. The return toprogramming may in some instances also be preceded by a commercial breakoutro which is a short video segment that indicates the return toprogramming. In some cases the intros and the outros may be the samewith an identical programming segment being used for both the intro andthe outro. Detecting the potential presence of the commercial breakintros or outros may indicate that an advertisement (or advertisementblock) is about to begin or end respectively. If the intros and/oroutros were always the same, detection could be done by detecting theexistence of specific video or audio, or specific logos or characters inthe video stream, or by detecting specific features about the videostream (e.g., CCVs). However, the intros and/or outros need not be thesame. The intros/outros may vary based on at least some subset of day,time, channel (network), program, and advertisement (or advertisementbreak).

Intros may be several frames of video easily recognized by the viewer,but may also be icons, graphics, text, or other representations that donot cover the entire screen or which are only shown for very briefperiods of time.

Increasingly, broadcasters are also selling sponsorship of certainprogramming which means that a sponsor's short message appears on eitherside (beginning or end) of each ad break during that programming. Thesesponsorship messages can also be used as latent cue tones indicating thestart and end of ad breaks.

The detection of the intros, outros, and/or sponsorship messages may bebased on comparing the incoming video stream, to a plurality of knownintros, outros, and/or sponsorship messages. This would require thateach of a plurality of known intros, outros, and/or sponsorship messagesbe stored and that the incoming video stream be compared to each. Thismay require a large amount of storage and may require significantprocessing as well, including the use of non-real-time processing. Suchstorage and processing may not be feasible or practical, especially forreal time detection systems. Moreover, storing the known advertisementsfor comparing to the video programming could potentially be considered acopyright violation.

The detection of the intros, outros, and/or sponsorship messages may bebased on detecting messages, logos or characters within the video streamand comparing them to a plurality of known messages, logos or charactersfrom known intros, outros, and/or sponsorship messages. The incomingvideo may be processed to find these messages, logos or characters. Theknown messages, logos or characters would need to be stored in advancealong with an association to an intro or outro. The comparison of thedetected messages, logos or characters to the known messages, logos orcharacters may require significant processing, including the use ofnon-real-time processing. Moreover, storing the known messages, logos orcharacters for comparison to messages, logos or characters from theincoming video stream could potentially be considered a copyrightviolation.

The detection of the intros, outros, and/or sponsorship messages may bebased on detecting messages within the video stream and determining themeaning of the words (e.g., detecting text in the video stream andanalyzing the text to determine if it means an advertisement is about tostart).

Alternatively, the detection may be based on calculating features(statistical parameters) about the incoming video stream. The featurescalculated may include, for example, color histograms or CCVs asdiscussed above. The features may be calculated for an entire videoframe, as discussed above, number of frames, or may be calculated forevenly/randomly highly subsampled representations of the video frame.For example, the video frame could be sampled at a number (e.g., 64) ofrandom locations or regions in the video frame and parameters such asaverage color) may be computed for each of these regions. Thesubsampling can also be performed in the temporal domain. The collectionof features including CCVs for a plurality of images/frames, colorhistograms for a plurality of regions, may be referred to as afingerprint.

An exemplary pixel grid 700 for a video frame is shown in FIG. 7. Forease of understanding, we limit the pixel grid to 12×12 (144 pixels),limit the color variations for each color (RGB) to the two mostsignificant bits (4 color variations), and have each pixel have the samenumber associated with each of the colors (RGB) so that a single numberrepresents each color. A plurality of regions 710, 720, 730, 740, 750,760, 770, 780, 785, 790, 795 of the pixel grid 700 are sampled and anaverage color for each of the regions 710, 720, 730, 740, 750, 760, 770,780, 785, 790, 795 is calculated. For example, the region 710 has anaverage color of 1.5, the region 790 has an average color of 0.5 and theregion 795 has an average color of 2.5, as shown in the average colorchart 705.

One advantage of the sampling of regions of a frame instead of an entireframe is that the entire frame would not need to be copied in order tocalculate the features (if copying was even needed to calculate thefeatures). Rather, certain regions of the image may be copied in orderto calculate the features for those regions. As the regions of the framewould provide only a partial image and could not be used to recreate theimage, there would be less potential copyright issues. As will bediscussed in more detail later, the generation of fingerprints for knownentities (e.g., advertisements, intros) that are stored in a databasefor comparison could be done for regions as well and therefore createless potential copyright issues.

Two exemplary pixel grids 800 and 810 are shown in FIG. 8. Each of thepixel grids is 11×11 (121 pixels) and is limited to binary color values(0 or 1) for simplicity reasons. The top view of each pixel grid 800,810 has a plurality of regions identified 815-850 and 855-890respectively. The lower view of each pixel grids 800, 810 has thecoherent 851-853 and 891-894 and incoherent pixels identified, where thethreshold level is greater than 5.

FIG. 9 shows exemplary comparisons of the pixel grids 800, 810 of FIG.8. Color histograms 900, 910 are for the entire frame 800, 810respectively and the difference in the color histograms ΔCH 920 is 0.CCVs 930, 940 are for the entire frame 800, 810 respectively and thedifference, ΔCCVs 950 is 0. Average colors 960, 970 capture the averagecolors for the various identified regions in frames 800, 810. Thedifference is the average color of the regions 980 is 3.5 (using the sumof absolute values).

FIGS. 7-9 focused on determining the average color for each of theregions but the techniques illustrated therein are not limited toaverage color determinations. For example, a color histogram or CCVcould be generated for each of these regions. For CCVs to provide usefulbenefits the regions would have to be big enough or all of the colorswill be incoherent. All of the colors will be coherent if the coherentthreshold is made too low.

The calculated features/fingerprints (e.g., CCVs, evenly/randomly highlysubsampled representations) are compared to correspondingfeatures/fingerprints for known intros and/or outros. The fingerprintsfor the known intros and outros could be calculated and stored inadvance. The comparison of calculated features of the incoming videostream (statistical parameterized representations) to the storedfingerprints for known intros/outros will be discussed in more detaillater.

Another method for detecting the presentation of an advertisement isautomatic detection of the advertisement. Automatic detection techniquesmay include recognizing that the incoming video stream is a knownadvertisement. Recognition techniques may include comparing the incomingvideo stream to known video advertisements. This would require that eachof a plurality of known video advertisements be stored in order to dothe comparison. This would require a relatively large amount of storageand would likely require significant processing, including non-real-timeprocessing. Such storage and processing may not be feasible orpractical, especially for real time detection systems. Moreover, storingthe known advertisements for comparison to the video programming couldpotentially be considered a copyright violation.

Accordingly, a more practical automatic advertisement recognitiontechnique may be used to calculate features (statistical parameters)about the incoming video stream and to compare the calculated featuresto a database of the same features (previously calculated) for knownadvertisements. The features may include color histograms, CCVs, and/orevenly/randomly highly subsampled representations of the video stream asdiscussed above or may include other features such as text and objectrecognition, logo or other graphic overlay recognition, and uniquespatial frequencies or patterns of spatial frequencies (e.g., salientpoints). The features may be calculated for images (e.g., frames) orportions of images (e.g., portions of frames). The features may becalculated for each image (e.g., all frames) or for certain images(e.g., every I-frame in an MPEG stream). The combination of features fordifferent images (or portions of images) make up a fingerprint. Thefingerprint (features created from multiple frames or frame portions)may include unique temporal characteristics instead of, or in additionto, the unique spatial characteristics of a single image.

The features/fingerprints for the known advertisements or other segmentsof programming (also referred to as known video entities) may have beenpre-calculated and stored at the detection point. For the knownadvertisements, the fingerprints may be calculated for the entireadvertisement so that the known advertisement fingerprint includescalculated features for the entire advertisement (e.g., every frame foran entire 30-second advertisement). Alternatively, the fingerprints maybe calculated for only a portion of the known advertisements (e.g., 5seconds). The portion should be large enough so that effective matchingto the calculated fingerprint for the incoming video stream is possible.For example, an effective match may require comparison of at least acertain number of images/frames (e.g., 10) as the false negatives may behigh if less comparison is performed.

An exemplary flowchart of the advertisement matching process is shown inFIG. 10. Initially, the video stream is received 1000. The receivedvideo stream may be analog or digital video. The processing may be donein either analog or digital but is computationally easier as digitalvideo (accordingly digital video may be preferred). Therefore, the videostream may be digitized 1010 if it is received as analog video. Features(statistical parameters) are calculated for the video stream 1020. Thefeatures may include CCVs, color histograms, other statisticalparameters, or a combination thereof. As mentioned above the featurescan be calculated for images or for portions of images. The calculatedfeatures/fingerprints are compared to corresponding fingerprints (e.g.,CCVs are compared to CCVs) for known advertisements 1030. According toone embodiment, the comparison is made to the pre-stored fingerprints ofa plurality of known advertisements (fingerprints of knownadvertisements stored in a database).

The comparison 1030 may be made to the entire fingerprint for the knownadvertisements, or may be made after comparing to some portion of thefingerprints (e.g., 1 second which is 25 frames in PAL or 29.97 framesin NTSC, 35 frames which is approximately 1.4 seconds in PAL) that islarge enough to make a determination regarding similarity. Adetermination is made as to whether the comparison was to entirefingerprints (or some large enough portion) 1040. If the entirefingerprint (or large enough portion) was not compared (1040 No)additional video stream will be received and have features calculatedand compared to the fingerprint (1000-1030). If the entire fingerprint(or large enough portion) was compared (1040 Yes) then a determinationis made as to whether the features of the incoming video stream meets athreshold level of similarity with any of the fingerprints 1050. If thefeatures for the incoming video stream do not meet a threshold level ofsimilarity with one of the known advertisement fingerprints (1050 No)then the incoming video stream is not associated with a knownadvertisement 1060. If the features for the incoming video stream meet athreshold level of similarity with one of the known advertisementfingerprints (1050 Yes) then the incoming video stream is associatedwith the known advertisement (the incoming video stream is assumed to bethe advertisement) 1070.

Once it is determined that the incoming video stream is anadvertisement, ad substitution may occur. Targeted advertisements may besubstituted in place of all advertisements within an advertisementblock. The targeted advertisements may be inserted in order or may beinserted based on any number of parameters including day, time, program,last time ads were inserted, and default advertisement (advertisement itis replacing). For example, a particular advertisement may be next inthe queue to be inserted as long as the incoming video stream is nottuned to a particular program (e.g., a Nike® ad may be next in the queuebut may be restricted from being substituted in football games becauseAdidas® is a sponsor of the football league). Alternatively, thetargeted advertisements may only be inserted in place of certain defaultadvertisements. The determination of which default ads should besubstituted with targeted ads may be based on the same or similarparameters as noted above with respect to the order of targeted adinsertion. For example, beer ads may not be substituted in a bar,especially if the bar sells that brand of beer. Conversely, if a defaultad for a competitor hotel is detected in the incoming video stream at ahotel the default ad should be replaced with a targeted ad.

The process described above with respect to FIG. 10 is focused ondetecting advertisements within the incoming video stream. However, theprocess is not limited to advertisements. For example, the same orsimilar process could be used to compare calculated features for theincoming video stream to a database of fingerprints for known intros (ifintros are used in the video delivery system) or known sponsorships (ifsponsorships are used). If a match is detected that would indicate thatan intro is being displayed and that an advertisement break is about tobegin. Ad substitution could begin once the intro is detected. Targetedadvertisements may be inserted for an entire advertisement block (e.g.,until an outro is detected). The targeted advertisements may be insertedin order or may be inserted based on any number of parameters includingday, time, program, and last time ads were inserted. Alternatively, thetargeted advertisements may only be inserted in place of certain defaultadvertisements. To limit insertion of targeted advertisements tospecific default advertisements would require the detection of specificadvertisements.

The intro or sponsorship may provide some insight as to what ads may beplayed in the advertisement block. For example, the intro detected maybe associated with (often played prior to) an advertisement break in asoccer game and the first ad played may normally be a beeradvertisement. This information could be used to limit the comparison ofthe incoming video stream to ad fingerprints for known beeradvertisements as stored in an indexed ad database or could be used toassist in the determination of which advertisement to substitute. Forexample, a restaurant that did not serve alcohol may want to replace thebeer advertisement with an advertisement for a non-alcoholic beverage.

The level of similarity is based on the minimal number of substitutions,deletions and insertions of features necessary to align the features ofthe incoming video stream with a fingerprint (called approximatessubstring matching). It is regarded as a match between the fingerprintsequences for the incoming video stream and a known advertisement if theminimal (absolute or relative to matched length) distance between doesnot exceed a distance threshold and the difference in length of thefingerprints does not exceed a length difference threshold. Approximatesubstring matching may allow detection of commercials that have beenslightly shortened or lengthened, or whose color characteristics havebeen affected by different modes or quality of transmission.

Advertisements only make up a portion of an incoming video stream sothat continually calculating features for the incoming video stream 1020and comparing the features to known advertisement fingerprints 1030 maynot be efficient. The feature based techniques described above (e.g.,volume increases, increase scene changes, monochrome images) may be usedto detect the start of a potential advertisement (or advertisementblock) and the calculating of features 1020 and comparing to knownfingerprints 1030 may only be performed once a possible advertisementbreak has been detected. It should be noted that some methods ofdetecting the possibility of an advertisement break in the video streamsuch as an increase in scene changes, where scene changes may bedetected by comparing successive CCVs, may in fact be calculatingfeatures of the video stream 1020 so the advertisement detection processmay begin with the comparison 1030.

The calculating of features 1020 and comparing to known fingerprints1030 may be limited to predicted advertisement break times (e.g.,between :10 and :20 after every hour). The generation 1020 and thecomparison 1030 may be based on the channel to which it is tuned. Forexample, a broadcast channel may have scheduled advertisement blocks sothat the generation 1020 and the comparison 1030 may be limited tospecific times. However, a live event such as a sporting event may nothave fixed advertisement blocks so time limiting may not be an option.Moreover channels are changed at random times, so time blocks would haveto be channel specific.

When intros are used, the calculated fingerprint for the incoming videostream may be continually compared to fingerprints for known introsstored in a database (known intro fingerprints). After an intro isdetected indicating that an advertisement (or advertisement block) isabout to begin, the comparison of the calculated fingerprint for theincoming video stream to fingerprints for known advertisements stored ina database (known advertisement fingerprints) begins.

If an actual advertisement detection is desired, a comparison of thecalculated fingerprints of the incoming video stream to the knownadvertisement fingerprints stored in a database will be performedwhether the comparison is continual or only after some event (e.g.,detection of intro, certain time). Comparing the calculated fingerprintof the incoming video stream to entire fingerprints (or portionsthereof) for all the known advertisement fingerprints 1030 may not be anefficient use of resources. The calculated fingerprint may have littleor no similarity with a percentage of the known advertisementfingerprints and this difference may be obvious early in the comparisonprocess. Accordingly, continuing to compare the calculated fingerprintto these known advertisement fingerprints is a waste of resources.

An initial window (e.g., several frames, several regions of a frame) ofthe calculated fingerprint of the incoming video steam may be comparedto an initial window of all of the known advertisement fingerprints(e.g., several frames, several regions). Only the known advertisementfingerprints that have less than some defined level of dissimilarity(e.g., less than a certain distance between them) proceed for furthercomparison. The initial window may be, for example, a certain period(e.g., 1 second), a certain number of images (e.g., first 5 I-frames),or a certain number of regions of a frame (e.g., 16 of 64 regions offrame).

FIG. 11 shows an exemplary flowchart of an initial dissimilaritydetermination process. The video stream is received 1100 and may bedigitized 1110 (e.g., if it is received as analog video). Features(statistical parameters) are calculated for the video stream (e.g.,digital video stream) 1120. The features (fingerprint) may include CCVs,color histograms, other statistical parameters, or a combinationthereof. The features can be calculated for images or for portions ofimages. The calculated features (fingerprint) are compared to thefingerprints for known advertisements 1130 (known advertisementfingerprints). A determination is made as to whether the comparison hasbeen completed for an initial period (window) 1140. If the initialwindow comparison is not complete (1140 No) the process returns to1100-1130. If the initial window comparison is complete (1140 Yes) thena determination is made as to the level of dissimilarity (distance)between the calculated fingerprint and the known advertisementfingerprints exceeding a threshold 1150. If the dissimilarity is belowthe threshold, the process proceeds to FIG. 10 (1090) for thosefingerprints. For the known advertisement fingerprints that thethreshold is exceeded (1150 Yes) the comparing is aborted.

FIG. 12 shows an exemplary initial comparison of the calculatedfingerprint for an incoming stream 1200 versus initial portions offingerprints 1210, 1220 for a plurality of known advertisements storedin a database (known advertisement fingerprints). For ease ofunderstanding we will assume that each color is limited to a singledigit (two colors), that each color has the same digit so that a singlenumber can represent each color, and that the pixel grid is 25 pixels.The calculated fingerprint includes a CCV for each image 1202, 1204, and1206 (e.g., frame, I-frame). The incoming video stream has a CCVcalculated for the first three frames. The CCV for the first threeframes of the incoming stream are compared to the associated portion1212-1216 and 1222-1226 (CCVs of the first three frames) of each of theknown advertisement fingerprints. The comparison includes summating thedissimilarity (e.g., calculated distance) between corresponding frames(e.g., distance Frame 1+distance Frame 2+distance Frame 3). The distancebetween the CCVs for each of the frames can be calculated in variousmanners including the sum of the absolute differences and the sum of thesquared differences as described above. The sum of the absolutedifferences is utilized in FIG. 12. The difference ΔCCV 1230 between theincoming video steam 1200 and a first fingerprint (FP₁) 1210 is 52 whilethe difference ΔCCV 1240 between the incoming video stream 1200 and theNth fingerprint (FP_(N)) 1220 is 8. Referring again to FIG. 11, if thepredefined level of dissimilarity (distance) was 25, then the comparisonfor FP₁ would not proceed further (e.g., 1160) since the level ofdissimilarity exceeds the predefined level (e.g., 1150 Yes). Thecomparison for FP_(N) would continue (1090) since the level ofdissimilarity did not exceed the predefined level (e.g., 1150 No).

It is possible that the incoming video stream may have dropped the firstfew frames of the advertisement or that the calculated features (e.g.,CCV) are not calculated for the beginning of the advertisement (e.g.,first few frames) because, for example, the possibility of anadvertisement being presented was not detected early enough. In thiscase, if the comparison of the calculated features for the first threeframes is compared to the associated portion (calculated features of thefirst three frames) of each of the known advertisement fingerprints, thelevel of dissimilarity may be increased erroneously since the frames donot correspond. One way to handle this is to extend the length of thefingerprint window in order to attempt to line the frames up.

FIG. 13 shows an exemplary initial comparison of calculated features foran incoming stream 1310 versus an expanded initial portion 1320 of knownadvertisement fingerprints. For ease of understanding one can make thesame assumptions as with regard to FIG. 12. The CCVs calculated for thefirst three frames 1312-1316 of the incoming video stream are comparedby a sliding window to the first five frames 1322-1329 for a storedfingerprint. That is, frames 1-3 of the calculated features of theincoming video stream are compared against frames 1-3 1322-1326 of thefingerprint, frames 2-4 1324-1328 of the fingerprint, and frames 3-51326-1329 of the fingerprint. By doing this it is possible to reduce oreliminate the differences that may have been caused by one or moreframes being dropped from the incoming video stream. In the example ofFIG. 13, the first two frames of the incoming stream were dropped.Accordingly, the difference 1350 between the calculated features of theincoming video stream equated best to frames 3-5 of the fingerprint.

If the comparison between the calculated features of the incoming streamand the fingerprint has less dissimilarity than the threshold, thecomparison continues. The comparison may continue from the portion ofthe fingerprint where the best match was found for the initialcomparison. In the exemplary comparison of FIG. 13, the comparisonshould continue between frame 6 (next frame outside of initial window)of the fingerprint and frame 4 of incoming stream. It should be notedthat if the comparison resulted in the best match for frames 1-3 of thefingerprint, then the comparison may continue starting at frame 4 (nextframe within the initial window) for the fingerprint.

To increase the efficiency by limiting the amount of comparisons beingperformed, the window of comparison may continually be increased for theknown advertisement fingerprints that do not meet or exceed thedissimilarity threshold until one of the known advertisementfingerprints possibly meets or exceeds the similarity threshold. Forexample, the window may be extended 5 frames for each knownadvertisement fingerprint that does not exceed the dissimilaritythreshold. The dissimilarity threshold may be measured in distance(e.g., total distance, average distance/frame). Comparison is stopped ifthe incoming video fingerprint and the known advertisement fingerprintdiffer by more than a chosen dissimilarity threshold. A determination ofa match would be based on a similarity threshold. A determination of thesimilarity threshold being met or exceeded may be delayed until somepredefined number of frames (e.g., 20) have been compared to ensure afalse match is not detected accidentally, which is more like with asmall number of frames. Like the dissimilarity threshold, the similaritythreshold may be measured in distance. For example, if the distancebetween the features for the incoming video stream and the fingerprintdiffer by less than 5 per frame after at least 20 frames are compared itis considered a match.

FIG. 14 shows an exemplary expanding window comparison of the featuresof the incoming video stream and the features of the fingerprints ofknown advertisements. For the initial window W₁ 1410, the incoming videostream 1450 is compared to each of five known advertisementfingerprints, FP₁-FP₅ 1455-1475, respectively. After W₁, the comparisonof FP₂ 1460 is aborted because it exceeded the dissimilarity threshold.The comparison of the remaining known advertisement fingerprintscontinues for the next window W₂ 1420 (e.g., next five frames, total of10 frames). After W₂, the comparison of FP₁ 1455 is aborted because itexceeded the dissimilarity threshold. The comparison of the remainingknown advertisement fingerprints continues for the next window W₃ 1430(e.g., next five frames, total of 15 frames). After W₃, the comparisonof FP₃ 1465 is aborted. The comparison of the remaining knownadvertisement fingerprints continues for the next window W₄ 1430 (e.g.,next five frames, total of 20 frames). After W₄, a determination can bemade about the level of similarity. As illustrated, it was determinedthat FP₅ 1475 meets the similarity threshold.

If neither of the known advertisement fingerprints (FP₄ or FP₅) meet thesimilarity threshold, the comparison would continue for the knownadvertisement fingerprints that did not exceed the dissimilaritythreshold. Those that meet the dissimilarity threshold would notcontinue with the comparisons. If more than one known advertisementfingerprint meets the similarity threshold then the comparison maycontinue until one of the known advertisement fingerprints falls outsideof the similarity threshold, or the most similar known advertisementfingerprint is chosen. The comparison always ends if the length of thecomparison reaches the length of the respective fingerprint.

The windows of comparison in FIG. 14 (e.g., 5 frames) may have been acomparison of temporal alignment of the frames, a summation of thedifferences between the individual frames, a summation of thedifferences of individual regions of the frames, or some combinationthereof. It should also be noted, that the window is not limited to acertain number of frames as illustrated and may be based on regions of aframe (e.g., 16 of the 32 regions the frame is divided into). If thewindow was for less than a frame, certain fingerprints may be excludedfrom further comparisons after comparing less than a frame. It should benoted that the level of dissimilarity may have to be high forcomparisons of less than a frame so as not to exclude comparisons thatare temporarily high due to, for example, misalignment of thefingerprints.

The calculated features for the incoming video stream do not need to bestored. Rather, they can be calculated, compared and then discarded. Novideo is being copied or if the video is being copied it is only for ashort time (temporarily) while the features are calculated. The featurescalculated for images can not be used to reconstruct the video, and thecalculated features are not copied or if the features are copied it isonly for a short time (temporarily) while the comparison to the knownadvertisement fingerprints is being performed.

As previously noted, the features may be calculated for an image (e.g.,frame) or for a portion or portions of an image. Calculating featuresfor a portion may entail sampling certain regions of an image asdiscussed above with respect to FIGS. 7-9 above. Calculating featuresfor a portion of an image may entail dividing the image into sections,selecting a specific portion of the image or excluding a specificportion of the image. Selecting specific portions may be done to focuson specific areas of the incoming video stream (e.g., network logo,channel identification, program identification). The focus on specificareas will be discussed in more detail later. Excluding specificportions may be done to avoid overlays (e.g., network logo) or banners(e.g., scrolling news, weather or sport updates) that may be placed onthe incoming video stream that could potentially affect the matching ofthe calculated features of the video stream to fingerprints, due to thefact that known advertisements might not have had these overlays and/orbanners when the original library fingerprints were generated.

FIG. 15 shows an exemplary pixel grid 1500 divided into sections 1510,1520, 1530, 1540 as indicated by the dotted line. The pixel grid 1500consists of 36 pixels (a 6×6 grid) and a single digit for each colorwith each pixel having the same number associated with each color. Thepixel grid 1500 is divided into 4 separate 3×3 grids 1510-1540. A fullimage CCV 1550 is generated for the entire grid 1500, and partial imageCCVs 1560, 1570, 1580, 1590 are generated for the associated sections1510-1540. A summation of the section CCVs 1595 would not result in theCCV 1550 as the pixels may have been coherent because they were groupedover section borders which would not be indicated in the summation CCV1595. It should be noted that the summation CCV 1595 is simply forcomparing to the CCV 1550 and would not be used in a comparison tofingerprints. When calculating CCVs for sections the coherence thresholdmay be lowered. For example, the coherence threshold for the overallgrid was four and may have been three for the sections. It should benoted that if it was lowered to 2 that the color 1 pixels in the lowerright corner of section pixel grid 1520 would be considered coherent andthe CCV would change accordingly to reflect this fact.

If the image is divided into sections, the comparison of the featuresassociated with the incoming video stream to the features associatedwith known advertisements may be done based on sections. The comparisonmay be based on a single section. Comparing a single section by itselfmay have less granularity than comparing an entire image.

FIG. 16 shows an exemplary comparison of two images 1600, 1620 based onthe whole images 1600, 1620 and sections of the images 1640, 1660 (e.g.,upper left quarter of image). Features (CCVs) 1610, 1630 are calculatedfor the images 1600, 1620 and reveal that the difference (distance)between them is 16 (based on sum of absolute values). Features (CCVs)1650, 1670 are calculated for the sections 1640, 1660 and reveal thatthere is no difference. The first sections 1640, 1660 of the images werethe same while the other sections were different thus comparing only thefeatures 1650, 1670 may erroneously result in not being filtered (notexceeding dissimilarity threshold) or a match (exceeding similaritythreshold). A match based on this false positive would not be likely, asin a preferred embodiment a match would be based on more than a singlecomparison of calculated features for a section of an image in anincoming video stream to portions of known advertisement fingerprints.Rather, the false positive would likely be filtered out as thecomparison was extended to further sections. In the example of FIG. 16,when the comparison is extended to other sections of the image or othersections of additional images the appropriate weeding out should occur.

It should be noted that comparing only a single section may provide theopposite result (being filtered or not matching) if the section beingcompared was the only section that was different and all the othersections were the same. The dissimilarity threshold will have to be setat an appropriate level to account for this possible effect or severalcomparisons will have to be made before a comparison can be terminateddue to a mismatch (exceeding dissimilarity threshold).

Alternatively, the comparison of the sections may be done at the sametime (e.g., features of sections 1-4 of the incoming video stream tofeatures of sections 1-4 of the known advertisements). As discussedabove, comparing features of sections may require thresholds (e.g.,coherence threshold) to be adjusted. Comparing each of the sectionsindividually may result in a finer granularity than comparing the wholeimage.

FIG. 17 shows an exemplary comparison of a pixel grid 1700 (divided intosections 1710, 1720, 1730, 1740) to the pixel grid 1500 (divided intosections 1510, 1520, 1530, 1540) of FIG. 15. By simply comparing thepixel grids 1500 and 1700 it can be seen that the color distribution isdifferent. However, comparing a CCV 1750 of the pixel grid 1700 and theCCV 1550 of the pixel grid 1500 results in a difference (distance) ofonly 4. However, comparing CCVs 1760-1790 for sections 1710-1740 to theCCVs 1560-1590 for sections 1510-1540 would result in differences of 12,12, 12 and 4 respectively, for a total difference of 40.

It should be noted that FIGS. 15-17 depicted the image being dividedinto four quadrants of equal size, but is not limited thereto. Ratherthe image could be divided in numerous ways without departing from thescope (e.g., row slices, column slices, sections of unequal size and/orshape). The image need not be divided in a manner in which the wholeimage is covered. For example, the image could be divided into aplurality of random regions as discussed above with respect to FIGS.7-9. In fact, the sections of an image that are analyzed and comparedare only a portion of the entire image and could not be used to recreatethe image so that there could clearly be no copyright issues. That is,certain portions of the image are not captured for calculating featuresor for comparing to associated portions of the known advertisementfingerprints that are stored in a database. The known advertisementfingerprints would also not be calculated for entire images but would becalculated for the same or similar portions of the images.

FIGS. 11-14 discussed comparing calculated features for the incomingvideo stream to windows (small portions) of the fingerprints at a timeso that likely mismatches need not be continually compared. The samebasic process can be used with segments. If the features for each of thesegments for an image are calculated and compared together (e.g., FIG.17) the process may be identical except for the fact that separatefeatures for an image are being compared instead of a single feature. Ifthe features for a subset of all the sections are generated andcompared, then the process may compare the features for that subset ofthe incoming video stream to the features for that subset of theadvertisement fingerprints. For the fingerprints that do not exceed thethreshold level of dissimilarity (e.g., 1150 No of FIG. 11) thecomparison window may be expanded to the additional segments of theimage and fingerprints or may be extended to the same section ofadditional images. When determining if there is a match between theincoming video stream and a fingerprint for a known ad (e.g., 1050 ofFIG. 10), the comparison is likely not based on a single section/regionas this may result in erroneous conclusions (as depicted in FIG. 16).Rather, it is preferable if the determination of a match is made aftersufficient comparisons of sections/regions (e.g., a plurality ofsections of an image, a plurality of images).

For example, a fingerprint for an incoming video stream (queryfingerprint q) may be based on an image (or portion of an image) andconsist of features calculated for different regions (q₁, q₂ . . .q_(n)) of the image. The fingerprints for known advertisements (subjectfingerprints s) may be based on images and consist of featurescalculated for different regions (s₁, s₂ . . . s_(m)) of the images. Theinteger m (the number of regions in an image for a stored fingerprint)may be greater than the integer n (number of regions in an image ofincoming video stream) if the fingerprint of the incoming video streamis not for a complete image. For example, regions may not be defined forboundaries on an incoming video stream due to the differences associatedwith presentation of images for different TVs and/or STBs. A comparisonof the fingerprints would (similarity measure) be the sum for i=1 to nof the minimum distance between q_(i) and s_(i), where i is theparticular region. Alternatively the Earth Movers distance could beused. The Earth Movers distance is defined as the minimal changesnecessary to transform the features in region q1, . . . , qn into thereference features of region s1, . . . , sm. This distance can usuallybe efficient compute by means of solving a special linear program calledthe optimal (partial) flow computation.

Some distance measures may not really be affected by calculating afingerprint (q) based on less than the whole image. However, it mightaccidentally match the wrong areas since some features such as colorhistograms may not encode any spatial distribution. For instance, areaswhich are visible in the top half of the incoming video stream and areused for the calculation of the query fingerprint might match an area ina subject fingerprint that is not part of the query fingerprint. Thiswould result in a false match. Such situations can be handled byincorporation of spatial constraints to the matching process.

As previously noted, entire images of neither the incoming video streamnor the known advertisements (ad intros, sponsorship messages, etc.) arestored, rather the portions of the images are captured so that thefeatures can be calculated. Moreover, the features calculated for theportions of the images of the incoming video stream are not stored, theyare calculated and compared to features for known advertisements andthen discarded.

If the video stream is an analog stream and it is desired to calculatethe features and compare to fingerprints in digital, then the videostream is converted to digital only as necessary. That is, if thecomparisons to fingerprints are done on an image by image basis theconversion to digital will be done image by image. If the video streamis not having features generated (e.g., CCV) or being compared to atleast one fingerprint then the digital conversion will not be performed.That is, if the features for the incoming video stream do not match anyfingerprints so no comparison is being done or the incoming video streamwas equated with an advertisement and the comparison is temporarilyterminated while the ad is being displayed or a targeted ad is beingsubstituted. If no features are being generated or compared then thereis no need for the digital conversion. Limiting the amount of conversionfrom analog to digital for the incoming video stream means that there isless manipulation and less temporary storage (if any is required) of theanalog stream while it is being converted.

When calculating the features for the incoming video stream certainsections (regions of interest) may be either avoided or focused on.Portions of an image that are excluded may be defined as regions ofdisinterest while regions that are focused on may be defined as regionsof interest. Regions of disinterest and/or interest may includeoverlays, bugs, and banners. The overlays, bugs and banners may includeat least some subset of channel and/or network logo, clock, sportsscoreboard, timer, program information, EPG screen, promotions, weatherreports, special news bulletins, close captioned data, and interactiveTV buttons.

If a bug (e.g., network logo) is placed on top of a video stream(including advertisements within the stream) the calculated features(e.g., CCVs) may be incomparable to fingerprints of the same videosequence (ads or intros) that were generated without the overlays.Accordingly, the overlay may be a region of disinterest that should beexcluded from calculations and comparisons.

FIGS. 18A and 18B illustrate exemplary images with different overlays.The two images 1810A, 1820A in FIG. 18A are taken from the same videostream. The first image 1810A has a channel logo overlay 1830A in theupper left corner and a promotion overlay 1840A in the upper rightcorner while the second image 1820A has no channel overlay and has adifferent promotion overlay 1850A. The two images 1810B, 1820B in FIG.18B are taken from the same video stream. The first image 1810B has astation overlay 1840B in the upper right corner and an interactivebottom 1830B in the lower right corner while the second image 1820B hasa different channel logo 1850B in the upper right and no interactivebutton. Comparing fingerprints for the first set of images or the secondset of images may result in a non-match due to the different overlays.

FIG. 19A shows an exemplary impact on pixel grids of an overlay beingplaced on a corresponding image. Pixel grid 1900A is for an image andpixel grid 1910A is for the image with an overlay. For ease ofexplanation and understanding the pixel grids are limited to 10×10 (100pixels) and each pixel has a single bit defining each of the RGB colors.The overlay was placed in the lower right corner of the image andaccordingly a lower right corner 1920A of the pixel grid 1910A wasaffected. Comparing the features (e.g., CCVS) 1930A, 1940A of the pixelgrids 1900A, 1910A respectively indicates that the difference (distance)1950A is 12 (using sum of absolute values).

FIG. 19A shows a system where the calculated fingerprint for theincoming video stream and the known advertisement fingerprints stored ina local database were calculated for entire frames. According to oneembodiment, the regions of disinterest (e.g., overlays, bugs or banners)are detected in the video stream and are excluded from the calculationof the fingerprint (e.g., CCVs) for the incoming video stream. Thedetection of regions of disinterest in the video stream will bediscussed in more detail later. Excluding the region from thefingerprint will affect the comparison of the calculated fingerprint tothe known advertisement fingerprints that may not have the regionexcluded.

FIG. 19B shows an exemplary pixel grid 1900B with the region of interest1910B (e.g., 1920A of FIG. 19A) excluded. The excluded region ofinterest 1910B is not used in calculating the features (e.g., CCV) ofthe pixel grid 1900B. As 6 pixels are in the excluded region of interest1910B, a CCV 1920B will only identify 94 pixels. Comparing the CCV 1920Bhaving the region of interest excluded and the CCV 1930A for the pixelgrid for the image without an overlay 1900A results in a difference1930B of 6 (using the sum of absolute values). By removing the region ofinterest from the difference (distance) calculation, the distancebetween the image with no overlay 1900A and the image with the overlayremoved 1900B was half of the difference between the image with nooverlay 1900A and the image with the overlay 1910A.

The regions of disinterest (ROD) may be detected by searching forcertain characteristics in the video stream. The search for thecharacteristics may be limited to locations where overlays, bugs andbanners may normally be placed (e.g., banner scrolling along bottom ofimage). The detection of the RODs may include comparing the image (orportions of it) to stored regions of interest. For example, networkoverlays may be stored and the incoming video stream may be compared tothe stored overlay to determine if an overlay is part of the videostream. Comparing actual images may require extensive memory for storingthe known regions of interest as well as extensive processing to comparethe incoming video stream to the stored regions.

A ROD may be detected by comparing a plurality of successive images. Ifa group of pixels is determined to not have changed for a predeterminednumber of frames, scene changes or hard cuts then it may be a logo orsome over type of overlay (e.g., logo, banner). Accordingly, the ROD maybe excluded from comparisons.

The known RODs may have features calculated (e.g., CCVs) and thesefeatures may be stored as ROD fingerprints. Features (e.g., CCVs) may begenerated for the incoming video stream and the video stream featuresmay be compared to the ROD fingerprints. As the ROD is likely small withrespect to the image the features for the incoming video stream may haveto be limited to specific portions (portions where the ROD is likely tobe). For example, bugs may normally be placed in a lower right handcorner so the features will be generated for a lower right portion ofthe incoming video and compared to the ROD fingerprints (at least theROD fingerprints associated with bugs) to determine if an overlay ispresent. Banners may be placed on the lower 10% of the image so thatfeatures would be generated for the bottom 10% of an incoming videostream and compared to the ROD fingerprints (at least the RODfingerprints for banners).

The detection of RODs may require that separate fingerprints begenerated for the incoming video stream and compared to distinctfingerprints for RODs. Moreover, the features calculated for thepossible RODs for the incoming video stream may not match stored RODfingerprints because the RODs for the incoming video stream may beoverlaid on top of the video stream so that the features calculated willinclude the video stream as well as the overlay where the knownfingerprint may be generated for simply the overlay or for the overlayover a different video stream. Accordingly it may not be practical todetermine RODs in an incoming video stream.

The generation of the fingerprints for known advertisements as well asfor the incoming video stream may exclude portions of an image that areknown to possibly contain RODs (e.g., overlays, banners). For example aspreviously discussed with respect to FIG. 19B, a possible ROD 1910B maybe excluded from the calculation of the fingerprint for the entireframe. This would be the case for both the calculated fingerprint of theincoming video stream as well as the known advertisement fingerprintsstored in the database. Accordingly, the possible ROD would be excludedfrom comparisons of the calculated fingerprint and the knownadvertisement fingerprints.

The excluded region may be identified in numerous manners. For example,the ROD may be specifically defined (e.g., exclude pixels 117-128). Theportion of the image that should be included in fingerprinting may bedefined (e.g., include pixels 1-116 and 129-150). The image may bebroken up into a plurality of blocks (e.g., 16×16 pixel grids) and thoseblocks that are included or excluded may be defined (e.g., includeregions 1-7 and 9-12, exclude region 6). A bit vector may be used toidentify the pixels and/or blocks that should be included or excludedfrom the fingerprint calculation (e.g., 0101100 may indicate that blocks2, 4 and 5 should be included and blocks 1, 3, 6 and 7 are excluded).

The RODs may also be excluded from sections and/or regions if thefingerprints are generated for portions of an image as opposed to anentire image as illustrated in FIG. 19B.

FIG. 20 shows an exemplary image 2000 to be fingerprinted that isdivided into four sections 2010-2040. The image 2000 may be from anincoming video stream or a known advertisement, intro, outro, or channelidentifier. It should be noted that the sections 2010-2040 do not makeup the entire image. That is, if each of these sections is grabbed inorder to create the fingerprint for the sections there is clearly nocopyright issues associated therewith as the entire image is notcaptured and the image could not be regenerated based on the portionsthereof. Each of the sections 2010-2040 is approximately 25% of theimage 2000, however the section 2040 has a portion 2050 excludedtherefrom as the portion 2050 may be associated with where an overlay isnormally placed.

FIG. 21 shows an exemplary image 2100 to be fingerprinted that isdivided into a plurality of regions 2110 that are evenly distributedacross the image 2100. Again it should be noted that the image 2100 maybe from an incoming video stream or a known advertisement and that theregions 2100 do not make up the entire image. A section 2120 of theimage may be associated with where a banner may normally be placed, thusthis portion of the image would be excluded. Certain regions 2130 fallwithin the section 2120 so they may be excluded from the fingerprint orthose regions 2130 may be shrunk so as to not fall within the section2120.

Ad substitution may be based on the particular channel that is beingdisplayed. That is, a particular targeted advertisement may not be ableto be displayed on a certain channel (e.g., an alcohol advertisement maynot be able to be displayed on a religious programming channel). Inaddition, if the local ad insertion unit is to respond properly tochannel specific cue tones that are centrally generated and distributedto each local site, the local unit has to know what channel is beingpassed through it. An advertisement detection unit may not have accessto data (e.g., specific frequency, metadata) indicating identity of thechannel that is being displayed. Accordingly the unit will need todetect the specific channel. Fingerprints may be defined for channelidentification information that may be transmitted within the videostream (e.g., channel logos, channel banners, channel messages) andthese fingerprints may be stored for comparison.

When the incoming video stream is received an attempt to identify theportion of the video stream containing the channel identificationinformation may be made. For example, channel overlays may normally beplaced in a specific location on the video stream so that portion of thevideo stream may be extracted and have features (e.g. CCV) generatedtherefore. These features will be compared to stored fingerprints forchannel logos. As previously noted, one problem may be the fact that thefeatures calculated for the region of interest for the video stream mayinclude the actual video stream as well as the overlay. Additionally,the logos may not be placed in the same place on the video stream at alltimes so that defining an exact portion of the video stream to calculatefeatures for may be difficult.

Channel changes may be detected and the channel information may bedetected during the channel change. The detection of a channel changemay be detected by comparing features of successive images of theincoming video stream and detecting a sudden and abrupt change infeatures. In digital programming a change in channel often results inthe display of several monochrome (e.g., blank, black, blue) frameswhile the new channel is decoded.

The display of these monochrome frames may be detected in order todetermine that a channel change is occurring. The display of thesemonochrome frames may be detected by calculating a fingerprint for theincoming video stream and comparing it to fingerprints for known channelchange events (e.g., monochrome images displayed between channelchanges). When channels are changed, the channel numbers may be overlaidon a portion of the video stream. Alternatively, a channel banneridentifying various aspects of the channel being changed to may bedisplayed. The channel numbers and/or channel banner may normally bedisplayed in the same location. As discussed above with respect to theRODs, the locations on the images that may be associated with a channeloverlay or channel banner may be excluded from the fingerprintcalculation. Accordingly, the fingerprints for either the incoming videostream or the channel change fingerprint(s) stored in the database wouldlikely be for simply a monochrome image.

An exemplary channel change image 2200 is show in FIGS. 22A and 22B. Theimage during a channel change is a monochrome frame 2210 with theexception of the channel change banner 2220 along the bottom of theimage. Accordingly, as shown in FIG. 22A, the entire channel banner 2220plus some tolerance may be identified as a region of disinterest 2230 tobe excluded from comparisons of the features generated for the incomingvideo stream and the stored fingerprints.

After, the channel change has been detected (whether based on comparingfingerprints or some other method), a determination as to what channelthe system is tuned to can be made. The determination may be based onanalyzing channel numbers overlaid on the image or the channel banner.The analysis may include comparing to stored channel numbers and/orchannel banners. As addressed above, the actual comparison of images orportions of images requires large amounts of storage and processing andmay not be possible to perform in real time.

Alternatively, features/fingerprints may be calculated for the incomingvideo stream and compared to fingerprints for known channelidentification data. As addressed above, calculating and comparingfingerprints for overlays may be difficult due to the background image.Accordingly, the calculation and comparison of fingerprints for channelnumbers will focus on the channel banners. It should be noted that thechannel banner may have more data than just the channel name or number.For example, it may include time, day, and program details (e.g., title,duration, actors, rating). The channel identification data is likelycontained in the same location of the channel banner so that only thatportion of the channel banner will be of interest and only that portionwill be analyzed.

FIG. 22B shows that the channel identification data 2240 is in the upperleft hand corner of the channel banner 2220. Accordingly, this areacontaining the channel identification data 2240 may be defined as aregion of interest. Fingerprints for the relevant portion of channelbanners for each channel will be generated and will be stored in adatabase. The channel identification fingerprints may be stored in samedatabase as the known advertisement (intro, outro, sponsorship message)fingerprints or may be stored in a separate database. If stored in thesame database the channel ident fingerprints are likely segregated sothat the incoming video stream is only compared to these fingerprintswhen a channel change has been detected.

It should be noted that different televisions and/or different set-topboxes may display an incoming video stream in slightly differentfashions. This includes the channel change banners 2220 and the channelnumber 2240 in the channel change banner being in different locations orbeing scaled differently. When looking at an entire image or multipleregions of an image this difference may be negligible in the comparison.However, when generating channel identification fingerprints for anincoming video stream and comparing the calculated channelidentification fingerprints to known channel identificationfingerprints, the difference in display may be significant.

FIG. 23 shows an image 2300 with expected locations of a channel banner2310 and channel identification information 2320 within the channelbanner 2310 identified. The channel identification information 2320 maynot be in the exact location expected due to parameters (e.g., scaling,translation) associated with the specific TV and/or STB (or DVR) used toreceive and view the programming. For example, it is possible that thechannel identification information 2320 could be located within aspecific region 2330 that is greatly expanded from the expected location2320.

In order to account for the possible differences, scaling andtranslation factors must be determined for the incoming video stream.These factors can be determined by comparing location of the channelbanner for the incoming video stream to the reference channel banner2310. Initially a determination will be made as to where an innerboundary between the monochrome background and the channel banner is.Once the inner boundary is determined, the width and length of thechannel banner can be determined. The scale factor can be determined bycomparing the actual dimensions to the expected dimensions. The scalefactor in x direction is the ratio of the actual width of the channelbanner and the reference width, the scale factor in y direction is theratio of the actual height of channel banner and the reference height.The translation factor can be determined based on comparing a certainpoint of the incoming stream to the same reference point (e.g., top leftcorner of the inner boundary between the monochrome background and thechannel banner).

The reference channel banner banners for the various channels are scaledand translated during the start-up procedure to the actual size andposition. The translation and scaling parameter are stored so they areknown. They can be used to scale and translate the incoming stream sothat an accurate comparison to the reference material (e.g.,fingerprints) can be made. The scaling and translation factors have beendiscussed with respect to the channel banner and channel identificationinformation but are in no way limited thereto. Rather, these factors canbe used to ensure an appropriate comparison of fingerprints of theincoming video stream to known fingerprints (e.g., ads, ad intros, adoutros, channel idents, sponsorships). These factors can also be used toensure that regions of disinterest or regions of interest are adequatelyidentified.

Alternatively, rather than creating a fingerprint for the channelidentifier region of interest the region of interest can be analyzed bya text recognition system that may recognize the text associated withthe channel identification data in order to determine the associatedchannel.

Some networks may send messages (‘channel ident’) identifying thenetwork (or channel) that is being displayed to reinforce network(channel) branding. According to one embodiment, these messages aredetected and analyzed to determine the channel. The analysis may becomparing the message to stored messages for known networks (channels).Alternatively, the analysis may be calculating features for the messageand comparing to stored features for known network (channel)messages/idents. The features may be generated for an entire videostream (entire image) or may be generated for a portion containing thebranding message. Alternatively, the analysis may include using textrecognition to determine what the message says and identifying thechannel based on that.

A maximum break duration can be identified and is the maximum amount oftime that the incoming video stream will be preempted. After this periodof time is up, insertion of advertisements will end and return to theincoming video stream. In addition a pre-outro time is identified. Apre-outro is a still or animation that is presented until the max breakduration is achieved or an outro is detected whichever is sooner. Forexample, the maximum break duration may be defined as 1:45 and thepre-outro may be defined as :15. Accordingly, three 30 secondadvertisements may be displayed during the first 1:30 of the ad breakand then the pre-outro may be displayed for the remaining :15 or untilan outro is detected, whichever is sooner. The maximum break durationand outro time are defined so as to attempt to prevent targetedadvertisements from being presented during programming. If an outro isdetected while advertisements are still being inserted (e.g., before thepre-outro begins) a return to the incoming video stream may beinitiated. As previously discussed sponsorship messages may be utilizedalong with or in place of outros prior to return of programming.Detection of a sponsorship message will also cause the return to theincoming video stream. Detection of programming may also cause thereturn to programming.

A minimum time between detection of a video entity (e.g., ad, ad intro)that starts advertisement insertion and ability to detect a video entity(e.g., ad outro, programming) that causes ad insertion to end can bedefined (minimum break duration). The minimum break duration may bebeneficial where intros and outros are the same. The minimum breakduration may be associated with a shortest advertisement period (e.g.,30 seconds). The minimum break duration would prevent the system fromdetecting an intro twice in a relatively short time frame and assumingthat the detection of the second was an outro and accordingly endinginsertion of an advertisement almost instantly.

A minimum duration between breaks (insertions) may be defined, and maybe beneficial where intros and outros are the same. The duration wouldcome into play when the maximum break duration was reached and thedisplay of the incoming video steam was reestablished before detectionof the outro. If the outro was detected when the incoming video streamwas being displayed it may be associated with an intro and attempt tostart another insertion. The minimum duration between breaks may also beuseful where video entities similar to know intros and/or outros areused during programming but are not followed by ad breaks. Such acondition may occur during replays of specific events during a sportingevent, or possibly during the beginning or ending of a program, whentitles and/or credits are being displayed.

The titles at the beginning of a program may contain sub-sequences orimages that are similar to know intros and/or outros. In order toprevent the detection of these sub-sequences or images from initiatingan ad break, the detection of programming can be used to suppress anydetection for a predefined time frame (minimum duration after programstart). The minimum duration after program start ensures that once thestart of a program is detected that sub-sequences or images that aresimilar to know intros and/or outros will not interrupt programming.

The detection of the beginning of programming (either the actualbeginning of the program or the return of programming after anadvertisement break) may end the insertion of targeted advertisements orthe pre-outro if the beginning of programming is identified before themaximum break duration is expired or an outro is identified.

Alternatively, if an outro, sponsorship message or programming isdetected during an advertisement being inserted, the advertisement maybe completed and then a return to programming may be initiated.

The detection of the beginning of programming may be detected bycomparing a calculated fingerprint of the incoming video stream withpreviously generated fingerprints for the programming. The fingerprintsfor programming may be for the scenes that are displayed during thetheme song, or a particular image that is displayed once programming isabout to resume (e.g., an image with the name of the program). Thefingerprints of programming and scenes within programming will bedefined in more detail below.

Once it is determined that programming is again being presented on theincoming video stream the generation and comparison of fingerprints maybe halted temporarily as it is unlikely that an advertisement break bepresented in a short time frame.

The detection of a channel change or an electronic program guide (EPG)activation may cause the insertion of advertisements to cease and thenew program or EPG to be displayed.

Fingerprints can be generated for special bulletins that may preemptadvertising in the incoming video stream and correspondingly would wantto preempt insertion of targeted advertising. Special bulletins maybegin with a standard image such as the station name and logo and thewords special bulletin or similar type slogan. Fingerprints would begenerated for each known special bulletin (one or more for each network)and stored locally. If the calculated fingerprint for an incoming videostream matched the special bulletin while targeted advertisement or thepre-outro was being displayed, a return to the incoming video streamwould be initiated.

While methods for local detection of advertisements or advertisementintros and local insertion of targeted advertisements have beendescribed, the methods described are not limited thereto. For example,certain programs may be detected locally. The local detection ofprograms may enable the automatic recording of the program on a digitalrecording device such as a DVR. Likewise, specific scenes or scenechanges may be detected. Based on the detection of scenes a programbeing recorded can be bookmarked for future viewing ease.

To detect a particular program, fingerprints may be established for aplurality of programs (e.g., video that plays weekly during theme song,program title displayed in the video stream) and calculated features forthe incoming video stream may be compared to these fingerprints. When amatch is detected the incoming video stream is associated with thatprogram. Once the association is made, a determination can be made as towhether this is a program of interest to the user. If the detectedprogram is a program of interest, a recording device may be turned on torecord the program. The use of fingerprints to detect the programs andensure they are recorded without any user interaction is an alternativeto using the electronic or interactive program guide to schedulerecordings. The recorded programs could be archived and indexed based onany number of parameters (e.g., program, genre, actor, channel,network).

Scene changes can be detected as described above through the matching offingerprints. If during recording of a program scene changes aredetected, the change in scenes can be bookmarked for ease of viewing ata later time. If specific scenes have already been identified andfingerprints stored for those scenes, fingerprints could be generatedfor the incoming video stream and compared against scene fingerprints.When a match is found the scene title could bookmark the scene beingrecorded.

The fingerprints stored locally may be updated as new fingerprints aregenerated for any combination of ads, ad intros, channel banners,program overlays, programs, and scenes. The updates may be downloadedautomatically at certain times (e.g., every night between 1 and 2 am),or may require a user to download fingerprints from a certain location(e.g., website) or any other means of updating. Automated distributionof fingerprints can also be utilized to ensure that viewers localfingerprint libraries are up-to-date.

The local detection system may track the features it generates for theincoming streams and if there is no match to a stored fingerprint, thesystem may determine that it is a new fingerprint and may store thefingerprint. For example, if the system detects that an advertisementbreak has started and generates a fingerprint for the ad (e.g., newPepsi® ad) and the features generated for the new ad are not alreadystored, the calculated features may be stored for the new ad.

In order to ensure that video segments (and in particular intros andadvertisements) are detected reliably, regions of interest in the videoprogramming are marked and regions outside of the regions of interestare excluded from processing. The marking of the regions of interest isalso used to focus processing on the areas that can provide informationthat is useful in determining to which channel the unit is tuned. In oneinstance, the region of interest for detection of video segments is theregion that is excluded for channel detection and visa versa. In thisinstance the area that provides graphics, icons or text indicating thechannel is examined for channel recognition but excluded for videosegment recognition.

Both feature based detection and recognition based detection methods canbe applied to video streams to recognize video entities as shown in FIG.24. Feature based detection methods 2400 can include, but are notlimited to, the detection of monochrome frames, the detection of scenebreak either through hard cuts or fades or the detection of actioneither through measurement of edge change ratios or motion vectorlengths. Recognition methods 2410 are based on fingerprints.Fingerprints can be created using both color histograms or CCVs based onthe entire image or can be generated based on subsampled representationswhere the subsampling occurs either spatially or temporally. AlthoughFIG. 24 shows the use of color histograms and CCVs, a number of otherstatistical parameters associated with the images or portions thereofcan be used to create fingerprints. Video entities can be considered tobe either known segments or sections of video which are of interest forautomatic detection. For example, advertisements, intros to sets ofadvertisements, promotions, still images representing advertisements,and other types of inserted advertising materials are video entities.Scene breaks including scene breaks leading to advertisements or scenebreaks simply placed between scenes of the programming can also beconsidered to be video entities. As such video entities representportions of the video stream that content providers may desire to keepintegral to the video stream but which automatic detection equipment maybe able to recognize for alteration of the video stream contrary to thewishes of the content provider.

The statistical properties of the compressed digital stream can also beused for video entity detection. Compression of a digital video streamcan comprise spatial and temporal compression techniques. FIG. 25 showsspatial compression of a digital video image, where an image frame 2510in an uncompressed state is subsequently transformed 2515 into afrequency domain representation. As will be understood by those skilledin the art, the image 2510 can be either a frame representing completeinformation within the image or a prediction error frame that containsonly partial information related to movement of macro blocks. Thespatial compression techniques can be applied to both full frames ofinformation as well as to prediction error frames. The transformed imagein the frequency domain can be represented in a table of coefficients2520, ranging from the DC coefficient 2530 in the upper left hand cornerto highest frequency component coefficients 2540 in the lower right handcorner, where vertical and horizontal frequencies vary along the X and Yaxis, respectively. A variety of techniques may be used to transform2515 the image from the spatial domain into the frequency domainincluding Discrete Cosine Transform (DCT), wavelet transforms, and avariety of other transforms which will result in frequency coefficientsthat represent the image.

The coefficients 2520 are scanned 2550 and weighted 2565 such thatparticular coefficients are given more importance than othercoefficients. This is due to the fact that the human eye will processthe image in a particular manner and that certain coefficients may bemore important than others and should thus be weighted accordingly forsubsequent compression steps and transmission. A quantizing step 2570 isused to reduce the length of some of the coefficients. Because some ofthe coefficients can be less important to the human eye, they arerepresented with fewer bits and thus a lower accuracy, which increaseswhat is termed as the quantizing error. This technique can be applied tocoefficients that are of less importance (usually the higher frequencycomponents) and reduce the amount of information which needs to betransmitted, thus achieving a coding gain or compression but which donot perceivably affect the image.

Once the image is appropriately weighted 2565 and quantized 2570, it canbe further encoded 2575 and output for either temporal coding, ortransmission if no temporal coding is being utilized as is also show inFIG. 25. For example, as will be understood to those skilled in the art,a Huffman coding algorithm (or arithmetic coding) can be used to furtherencode the compressed image data in the spatial domain. The quantizedcoefficients 2585 representing an uncompressed digital image can berepresented all together in a one dimensional sequence. In thissequence, each non-zero coefficient is known as a “size”, and a numberof successive zero value coefficients preceding a size value is known asa “run”. FIG. 26 shows a table 2610 where size and run length parametersare converted to a particular Huffman code word 2620 for transmission.The particular code word to be transmitted is dependent on both thenumber of zeros that have previously appeared and the particularcoefficient that is then needed to be transmitted. Although that table2610 represents a particular assignment of codes, any number of codingschemes can be used, those coding schemes being able to efficiently codethe information such that transmission bandwidth is minimized and suchthat the information is appropriately transmitted. In some instances,codes will be used that are particularly robust and can survive loss ofcertain bits of information, while in other instances the goal willsimply be that of compression.

The entry in the size column of table 2610 is a coefficient that in someinstances is a 1010 coefficient 2630 and which represents a pattern forthe End of Blocks (EOB) symbol. This code word is assigned to thezero/zero entry in the table since the code of zero/zero has nomeaningful run-size interpretation. Use of this code word for end ofblock allows easy determination of the end of a block in the spatialcompressed digital video stream.

As is also shown in FIG. 25, there are statistical parameters related tothe coefficients 2520 after transformation as well as statisticalparameters related to the weighted coefficients 2560. The statisticalparameters of the coefficients or weighted coefficients can be used as amethod of understanding the image and, in particular, of fingerprintingthe image. For example, an image that has been compressed will have acertain histogram of coefficients 2570 that can be used as afingerprint. Likewise, a histogram of the weighted coefficients 2580 canbe used to create a fingerprint for an image. In the event that thisfingerprint of the statistical parameters of compression are recordedand stored, the incoming video can be checked against the stored videoby comparing the statistical parameters of the incoming video with thestatistical compression parameters of the stored video.

The statistical parameters of the code words can also be used forfingerprinting. FIG. 28 shows the use of code word histograms forfingerprinting by showing an actual possible transmission stream 2810with an end of block 2820 followed by a series of code words 2830,followed by another end of block 2860 followed by a series of code words2850. Other statistical parameters that can be used in addition to thosepreviously mentioned include the time separation between the end ofblocks 2870, the histogram of the end of block separation 2880 and thecode word histogram 2890. There will be statistical variations such thata video segment of several seconds or 30 seconds in length will have aparticular code word histogram which can be potentially used to identifythat video segment. In some instances looking at a small portion of thevideo segments such as a few milliseconds of video sequence will createa code word histogram that is sufficiently unique to allow that codeword histogram to be used for either a full or partial identification ofthat video sequence.

A spatially transformed and compressed digital video sequence can befurther temporally compressed by removing redundant information in timeand creating representations that are of only a partial frame, but allowreconstruction of entire frame. For example in MPEG-2, I-frames are usedin conjunction with B- (bidirectional) and P- (predictive) frames toallow efficient transmission of the video without requiring transmissionof the full video image in each and every frame. FIG. 29 shows a videosequence in which temporal compression has taken place. In a framesequence 2910, an I-frame can be followed by a number of B-frames andP-frame, other B-frames, P-frames and then a subsequent I frame. Theactual image that is produced is based on a reconstruction from theB-frames and P-frames in conjunction with the I-frames or based on theI-frames alone. The statistically relevant parameters that occur includethe separation of the I-frames 2920 as well as the actual statistics ofthe B-, P- and I-frames between I frames. As such, a statisticallyrelevant fingerprint based on the compression of the video can becreated by looking at a histogram of the I-frame separation 2930 or ofthe number of B-, P- and I-frames within a given time segment 2940. Forexample, it is possible to measure on a sub frame or full frame basisthe statistics of that frame and compare those statistics againststatistics of known video sequences which have been fingerprinted. Inthe event that there is sufficient similarity between the incomingstatistics of the compressed incoming video sequence, they can becompared against the statistics of the known sequences and adetermination made that the sequences match or that additional analysisneeds to be performed in the uncompressed domain to correctly identifythe video sequence. Although the ability to use frame histograms willdepend to some extent on the use of similar encoders, there are certainframe statistics that will have strong similarities between differentencoders.

The statistics associated with the motion vectors can also be analyzedand used as the basis for a fingerprint. In particular, the statisticsof the magnitude and direction of the motion vectors can provide thebasis for a unique characterization and fingerprinting of a frame.

In one embodiment, it is possible to generate spatially reduced imagesfrom MPEG-1, MPEG-2, MPEG-4, VC-1 (video codec based on MicrosoftWindows Media Video version 9) or other compressed digital videostreams. These spatially reduced images can be derived directly fromI-frames, or approximated from P-frames and B-frames. In one embodimentthe approximation from P-frames and B-frames is accomplished byemploying motion information to derive the DC images. A zero-orderapproximation can be obtained by taking the DC value from the block inthe P-frame or B-frame that has the most overlap with a reference block,the reference block being the current block of interest. A first-orderapproximation can be determined by weighing the contributions from the 4neighboring DC values with the ratio of overlaps of the reference blockwith each of the neighboring blocks. These techniques can be applied togenerate DC images from frames of compressed video. Once DC images havebeen obtained, it is possible to generate fingerprints from those DCimages, and to compare those fingerprints against stored fingerprints.Because the processing can be performed at high speeds, it is possibleto generate fingerprints at rates equal to or exceeding 1,000 frames persecond.

In one embodiment, shown in FIG. 27, synchronization points aredetermined within a compressed digital video stream. Upon detection of asynchronization point 2720 after receiving a stream 2710, a fingerprintcomprising of a statistical parameterized representation of thecompressed stream is created for a window following the synchronizationpoint 2730. The fingerprint of the incoming stream is compared with aplurality of fingerprints based on previously parameterized known videoentities 2740. If the parameterized representation of the incomingstream has at least a threshold level of similarity 2750 with aparticular fingerprint in the plurality of fingerprints then detectionof the known video entity within the incoming stream is accomplished2760. To increase the level of confidence for detection of the knownvideo entity, further processing of the uncompressed video andsubsequent comparison to a plurality of fingerprints can be performed.When the level of confidence of known video entity detection issufficiently high, either with or without additional processing in theuncompressed domain, further action, such as insertion of a targetedadvertisement into a presentation stream, can be accomplished.

Synchronization points are determined from time stamps or clockreferences embedded within a compressed digital video stream. It will beunderstood by one skilled in that art, that synchronization points fordigital streams encoded using different standards can be obtained fromtime information encoded within those streams. In one embodiment, asynchronization point 3090 in an MPEG-2 steam 3070 can be determined asshown in FIG. 30. The compressed steam 3070 may contain time stamps thatindicate to the decoder 3085 to decode and uncompress the compressedvideo data to create a presentation scene and time stamps which indicatethe decoded, uncompressed scene is ready to be output. A DTS (decodingtime stamp) 3080 indicates when a presentation scene is to be createdand a PTS (presentation time stamp) 3075 indicates when a presentationscene is to be output. Additionally, the decoder 3085 can also determinea synchronization point 3090 based on a DTS and a PTS contained withinthe stream.

In an alternate embodiment, a synchronization point 3060 is determinedfrom time stamps or clock references embedded within a compresseddigital video stream in the MPEG-4 format 3010 as shown in FIG. 30. Thecompressed steam 3010 contains time stamps in the BIFS commands thatindicate to the decoder to decode and uncompress the compressed videodata to create a presentation scene 3020 and time stamps which indicatethe decoded, uncompressed scene is ready to be output 3030, or likewise,terminate output of a presentation scene 3040 in the presentation stream3050. FIG. 30 shows a synchronization point associated with the commandto create a scene from the compressed steam, although determination ofsynchronization points within the stream are not limited to thisassociation with a scene creation command.

Based on the detection of synchronization points, fingerprints can beimmediately generated from the statistical parameters of the compresseddigital stream and compared against fingerprints of the statisticalparameters in a stored library. One advantage of the approach describedherein is that the ability to detect synchronization points within thecompressed digital video stream provides for partial temporal alignmentof the fingerprints generated in the incoming video stream with thefingerprints in the library. As a result of this partial temporalalignment, it is no longer necessary to completely cross-correlate frameor sub-frame fingerprints for each in-coming against each and everyframe or sub-frame fingerprint of a stored frame. Instead, the timingobtained from the compressed digital video stream is used to identifypotential advertisements, and the comparison is made between thebeginning of what is believed to be an advertisement and the beginningof an actual stored advertisement. As such, the computationalrequirements for fingerprint comparison are reduced.

In one embodiment, based on the detection of a synchronization point,fingerprints can be immediately generated from the statisticalparameterized representations of the compressed digital stream,including at least some subset of coefficient histograms, weightedcoefficient histograms, code word histograms, histograms of separationof end blocks, histograms of I frame separations, the number of B, P,and I frames within a time segment, motion compensation vectors, andspatially reduced coefficients.

In one embodiment, based on the detection of a synchronization point,fingerprints can be immediately generated from the DC coefficients ofthe I-frames which represent a low resolution images of the imagerepresented by the I frame. Based on the detection of thesynchronization points and generation of DC coefficients from thecompressed digital video stream, a series of fingerprints can begenerated and compared against fingerprints in a stored library.

In an alternate embodiment, the stored fingerprints are based on both ACand DC coefficients in a linear mixture and do not contain recognizableimages. The incoming compressed video stream is used to generate similarfingerprints that are linear combinations of AC and DC coefficientswhich are compared against the stored fingerprints. In this embodimentno actual images exist or are stored in the fingerprinting system, thusminimizing copyright issues.

In another embodiment, video entity recognition is performed in thecompressed digital video domain but additional processing is utilizedbased on the uncompressed images to confirm the presence of anadvertisement. In this embodiment, the dual processing (compressed anduncompressed domains) provides for a higher reliability of video entityor advertisement recognition.

The present method and system allows for a fingerprinting of videosequences based on the statistical parameters of compression. Thefingerprints that are created are then compared against the statisticalparameters of an incoming video stream to detect a known video sequence.These parameters include the statistical parameters generated both inthe spatial compression as well as that in the temporal compression. Thespatial compression parameters include coefficient histograms, weightinghistograms, quantization statistics as well as any other statisticsrelated to the spatial compression process. Similarly in the temporaldomain, the statistics related to the creation of the temporalcompression including the number of I, B and P frames, the spacingbetween I frames, and other parameters related to the motioncompensation can all be used to create fingerprints and subsequentlyrecognize video sequences. Motion compensation vectors and thestatistics thereof can be used as a means of creating a fingerprint andsubsequent comparison between incoming motion compensation vectors andthe fingerprint to determine if the images match. Fingerprints can alsobe obtained from spatially reduced images obtained from the DCcoefficients, or similarly a linear combination of DC and ACcoefficients, of the compressed digital video image.

The techniques described herein can be applied to a variety of videocompression techniques including, but not limited to, MPEG-1, MPEG-2 andMPEG-4. In MPEG-4, a number of additional statistical parameters areavailable including the parameters relating to video objects, stilltexture objects, mesh objects and face and body animation objects. Thestatistics related to those objects include differential encodingstatistics including the vectors and residuals, the statistics relatedto what are known as the video object planes and other parametersrelated specific to MPEG-4 encoding. These parameters can be derivedfrom the compression and decompression of the MPEG-4 and be used tocreate fingerprints and to recognize those fingerprints just as appliedto MPEG- 2.

It is noted that any and/or all of the above embodiments,configurations, and/or variations of the present invention describedabove can be mixed and matched and used in any combination with oneanother. Moreover, any description of a component or embodiment hereinalso includes hardware, software, and configurations which already existin the prior art and may be necessary to the operation of suchcomponent(s) or embodiment(s).

All embodiments of the present invention, can be realized in on a numberof hardware and software platforms including microprocessor systemsprogrammed in languages including (but not limited to) C, C++, Perl,HTML, Pascal, and Java, although the scope of the invention is notlimited by the choice of a particular hardware platform, programminglanguage or tool.

The present invention may be implemented with any combination ofhardware and software. If implemented as a computer-implementedapparatus, the present invention is implemented using means forperforming all of the steps and functions described above.

The present invention can be included in an article of manufacture(e.g., one or more computer program products) having, for instance,computer useable media. The media has embodied therein, for instance,computer readable program code means for providing and facilitating themechanisms of the present invention. The article of manufacture can beincluded as part of a computer system or sold separately.

The many features and advantages of the invention are apparent from thedetailed specification. Thus, the appended claims are to cover all suchfeatures and advantages of the invention that fall within the truespirit and scope of the invention. Furthermore, since numerousmodifications and variations will readily occur to those skilled in theart, it is not desired to limit the invention to the exact constructionand operation illustrated and described. Accordingly, appropriatemodifications and equivalents may be included within the scope.

1. A method of detecting a known video entity within a compresseddigital video stream, the method comprising: (a) receiving thecompressed digital video stream; (b) determining synchronization pointswithin the compressed digital video stream; (c) creating statisticalparameterized representations of the compressed digital video stream forwindows following the synchronization points in the video stream; (d)comparing the statistical parameterized representation windows towindows of a plurality of fingerprints, wherein each of the plurality offingerprints includes associated statistical parameterizedrepresentations of a known video entity; and (e) detecting a known videoentity in the compressed digital video stream when at least one of theplurality of fingerprints has at least a threshold level of similarityto a fingerprint of the video stream.
 2. The method of claim 1, whereinthe synchronization points are obtained using timing indicators withinthe compressed digital video stream.
 3. The method of claim 1, whereinthe compressed digital video stream is spatially compressed.
 4. Themethod of claim 1, wherein the statistical parameterized representationsare based on coefficient histograms.
 5. The method of claim 1, whereinthe statistical parameterized representations are based on weightedcoefficient histograms.
 6. The method of claim 1, wherein thestatistical parameterized representations are based on code wordhistograms.
 7. The method of claim 1, wherein the statisticalparameterized representations are based on histograms of the separationof the end of blocks.
 8. The method of claim 1, wherein the compresseddigital video stream is temporally compressed.
 9. The method of claim 1,wherein the statistical parameterized representations are based onhistograms of I frame separations.
 10. The method of claim 1, whereinthe statistical parameterized representations are based on the number ofB, P, and I frames within a time segment.
 11. The method of claim 1,wherein the statistical parameterized representations are based onmotion compensation vectors.
 12. The method of claim 1, wherein thestatistical parameterized representations are spatially reducedcoefficients.
 13. The method of claim 12, wherein the spatially reducedcoefficients are based on the DC coefficients derived from I, P, or Bframes of the compressed digital video stream.
 14. The method of claim12, wherein the spatially reduced coefficients are based on a linearcombination of DC and AC coefficients derived from I, P, or B frames ofthe compressed digital video stream.
 15. The method of claim 1, furthercomprising: (f) processing uncompressed video when a particularfingerprint of the plurality of fingerprints has at least a thresholdlevel of similarity to the video stream.
 16. The method of claim 1,wherein the known video entities are advertisements.
 17. The method ofclaim 1, wherein the known video entities are at least some subset ofadvertisement intros, advertisement outros, channel idents, andsponsorships.
 18. The method of claim 1, wherein said detecting of step(e) includes detecting an advertisement opportunity in the video streamwhen said comparing of step (d) indicates that a fingerprint associatedwith an advertisement or an advertisement intro has at least a thresholdlevel of similarity with the statistical parameterized representationsof the video stream.
 19. The method of claim 1, further comprising: (f)inserting a targeted advertisement subsequent to said detecting anadvertisement opportunity in the video stream.
 20. A method fordetecting a known video entity within a video stream, the methodcomprising: receiving a video stream; continually creating statisticalparameterized representations for windows of the video stream;continually comparing the statistical parameterized representationwindows to windows of a plurality of fingerprints, wherein each of theplurality of fingerprints includes associated statistical parameterizedrepresentations of a known video entity; and detecting a known videoentity in the video stream when a particular fingerprint of theplurality of fingerprints has at least a threshold level of similaritywith the video stream.
 21. A system for detecting a known video entitywithin a compressed digital video stream, the system comprising: areceiver to receive a compressed digital video stream; memory forstoring a plurality of fingerprints, wherein each of the plurality offingerprints includes associated statistical parameterizedrepresentations of a known video entity; and a processor configured todetermine synchronization points within a compressed digital videostream; create statistical parameterized representations of thecompressed digital video stream for windows following thesynchronization points; compare the statistical parameterizedrepresentation windows following the synchronization points to windowsof the plurality of fingerprints; and detect a known video entity in thevideo stream when at least one of the plurality of fingerprints has atleast a threshold level of similarity to a fingerprint of the videostream.
 22. The system of claim 21, wherein the synchronization pointsare obtained points are obtained using timing indicators within thecompressed digital video stream.
 23. The system of claim 21, wherein thestatistical parameterized representations include at least some subsetof coefficient histograms, weighted coefficient histograms, code wordhistograms, histograms of separation of end blocks, histograms of Iframe separations, the number of B, P, and I frames within a timesegment, motion compensation vectors, and spatially reducedcoefficients.
 24. The system of claim 21, wherein the known videosegments include at least some subset of advertisements, advertisementintros, sponsorship messages, advertisement outros, and channel idents.25. The system of claim 21, wherein said processor detects anadvertisement opportunity in the video stream when the comparingindicates that a fingerprint associated with an advertisement or anadvertisement intro has at least a threshold level of similarity withthe statistical parameterized representations of the video stream. 26.The system of claim 25, further comprising: a video inserter to inserttargeted advertisements once the incoming video stream is associatedwith a known advertisement.
 27. An article of manufacture for detectinga known video entity within a compressed digital video stream, thearticle of manufacture comprising a computer-readable medium holdingcomputer-executable instructions for performing a method comprising: (a)receiving the compressed digital video stream; (b) determiningsynchronization points within the compressed digital video stream; (c)creating statistical parameterized representations of the compresseddigital video stream for windows following the synchronization points inthe video stream; (d) comparing the statistical parameterizedrepresentation windows to windows of a plurality of fingerprints,wherein each of the plurality of fingerprints includes associatedstatistical parameterized representations of a known video entity; and(e) detecting a known video entity in the compressed digital videostream when at least one of the plurality of fingerprints has at least athreshold level of similarity to a fingerprint of the video stream. 28.The article of manufacture of claim 27, the computer-executableinstructions performing a method wherein the synchronization points areobtained using timing indicators within the compressed digital videostream.
 29. The article of manufacture of claim 27, thecomputer-executable instructions performing a method wherein thecompressed digital video stream is spatially compressed.
 30. The articleof manufacture of claim 27, the computer-executable instructionsperforming a method wherein the statistical parameterizedrepresentations include at least some subset of coefficient histograms,weighted coefficient histograms, code word histograms, histograms ofseparation of end blocks, histograms of I frame separations, the numberof B, P, and I frames within a time segment, motion compensationvectors, and spatially reduced coefficients.
 31. The article ofmanufacture of claim 27, the computer-executable instructions performinga method further comprising: (f) processing uncompressed video when aparticular fingerprint of the plurality of fingerprints has at least athreshold level of similarity to the video stream.
 32. The article ofmanufacture of claim 27, the computer-executable instructions performinga method wherein the known video entities are advertisements.
 33. Thearticle of manufacture of claim 27, the computer-executable instructionsperforming a method wherein the known video entities are at least somesubset of advertisement intros, advertisement outros, channel idents,and sponsorships.
 34. The article of manufacture of claim 27, thecomputer-executable instructions performing a method wherein saiddetecting of step (e) includes detecting an advertisement opportunity inthe video stream when said comparing of step (d) indicates that afingerprint associated with an advertisement or an advertisement introhas at least a threshold level of similarity with the statisticalparameterized representations of the video stream.
 35. The article ofmanufacture of claim 27, the computer-executable instructions performinga method further comprising: (f) inserting a targeted advertisementsubsequent to said detecting an advertisement opportunity in the videostream.